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Int. J. Process Management and Benchmarking, Vol. 3, No. 2, 2013 173
Copyright © 2013 Inderscience Enterprises Ltd.
An assessment of location data requirements in logistics
Ahmed Musa* Lancashire Business School, University of Central Lancashire, Preston PR1 2HE, UK Fax: +44(0)1772892906 E-mail: amusa@uclan.ac.uk *Corresponding author
Angappa Gunasekaran Department of Decision and Information Sciences, Charlton College of Business, University of Massachusetts-Dartmouth, North Dartmouth, MA 02747-2300, USA E-mail: agunasekaran@umassd.edu
Yahaya Yusuf Lancashire Business School, University of Central Lancashire, Preston PR1 2HE, UK E-mail: yyusuf@uclan.ac.uk
Samuel Azua and Youngu Terwase Department of Geomatics, Ahmadu Bello University, Zaria 880001, Nigeria E-mail: sadzua@gmail.com E-mail: terwasey2000@gmail.com
Abstract: In the geolocation and navigation community, requirements for location data are often expressed in terms of accuracy, integrity and availability of location information in both time and space. The question arises as to what these requirements are across the application spectrum in logistics and the technologies available to meet the needs. The answer to this question is not as straightforward as it might first seem, given the diversity of potential applications and the plethora of technologies currently available, or are being developed, to meet various industrial needs. Based on a specially-designed Delphi study with experts across industries, this paper provides some specific information and guidance in this regard. The results presented in this paper can be used by the logistics, geolocation and semiconductor industries alike in identifying the geolocation needs of their various applications and how those needs can be fulfilled.
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Keywords: location awareness; logistics; supply chain management; RFID; tracking and tracing.
Reference to this paper should be made as follows: Musa, A., Gunasekaran, A., Yusuf, Y., Azua, S. and Terwase, Y. (2013) ‘An assessment of location data requirements in logistics’, Int. J. Process Management and Benchmarking, Vol. 3, No. 2, pp.173–212.
Biographical notes: Ahmed Musa holds a PhD from the University of Newcastle upon Tyne, UK. He is a Researcher in Logistics and Operations at Lancashire Business School, University of Central Lancashire, Preston, UK, and a member of the University’s Institute of Logistics and Operations Management. He has research experience in engineering and management. Currently, his research activities are focused on agility, risk and resilience in strategic networks; oil and gas operations; energy supply chains; and optimal and autonomous control in logistics and operations.
Angappa Gunasekaran is the Dean of Charlton College of Business, University of Massachusetts Dartmouth, USA. He received his PhD in Industrial Engineering and Operations Research from the Indian Institute of Technology, Bombay. He was the Chairperson of the Department of Decision and Information Sciences, Charlton College of Business, from 2006 to 2012. His articles have been cited in over 13,000 articles, most of which are in prestigious journals. He is the Editor-in-Chief of several journals and has guest-edited special issues for a number of highly rated journals. He is also on the editorial boards of more than 20 peer-reviewed journals.
Yahaya Yusuf holds a PhD in Operations Management from the University of Liverpool, UK. He is a Professor at Lancashire Business School, University of Central Lancashire, Preston, UK, and the Director of the University’s Institute of Logistics and Operations Management. His main research interests are in agile manufacturing, supply chain agility, sustainable operations and supply chains, and decision support systems for logistics and operations management. His research has been supported by grants from the UK’s Engineering and Physical Science Research Council (EPSRC) and industry. He is a member of the EPSRC Peer Review College and on the editorial boards of three prestigious journals.
Samuel Azua is a Lecturer in the Department of Geomatics, Ahmadu Bello University, Zaria, Nigeria. He holds a Master’s degree from the University of Lagos, Nigeria, and is currently a PhD student in the West African Regional Centre for Training in Aerospace Surveys, Ile-Ife, Nigeria. His current research interests include location determination by satellite signals, and use of geospatial data for environmental, social and economic studies.
Youngu Terwase is a Lecturer in the Department of Geomatics, Ahmadu Bello University, Zaria, Nigeria. He holds a Master’s degree from the University of Lagos, Nigeria, and is currently a PhD student in Ahmadu Bello University, Zaria. His current research interests include high-precision and mobile-computing related positioning using hybrid technologies.
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1 Introduction
The geographic location of an object may be defined with respect to a global or local frame of reference. The frame of reference itself may be based on any given system of coordinate, examples of which include rectangular (Cartesian), spherical, ellipsoidal, and physical (gravitational potential) coordinate system (Moon and Spencer, 1988). By ‘location data’, we mean the following equation:
location data location coordinates accuracy continuity availability= + + +
The accuracy of location reflects how close its coordinates are to their true/errorless value. In some cases it may simply be an indication of the internal consistency of the measurement system, while in other situations it may represent a deviation of the measured value from a known external standard. The integrity of location is usually a reflection of the confidence that is associated with the reported value. Confidence may be expressed by the probability that the given value is within the true value, such as saying the location is known to twice its standard deviation 95% of the time. Continuity implies that the location value will not change until a certain time, if ever, despite changes in the ambient measurement environment (e.g., weather). Availability means that the infrastructure (e.g., satellite signals) used to determine the location will be available at some point in the future to repeat the measurements that led to the location value. Continuity and availability both indicate the repeatability of the given location value.
Geolocation is the identification of the geographic location of an object, such as a vehicle, mobile phone, or an internet-connected computer terminal. Geolocation may refer to the routine of evaluating the location, or to the actual measured location. Geolocation is closely related to the use of positioning systems and technologies to find location, but location can be discriminated from positioning by, for instance, a greater emphasis on determining a meaningful location (e.g., a street address) rather than just a set of geographic coordinates. Geolocation is also defined in the terms and definitions standardised by ISO/IEC 19762-5:2008 for automatic identification and data capture (AIDC) techniques (ISO/IEC, 2008).
For either geolocating or positioning, a range of methods and technologies are available to find the location, the best known today perhaps being GPS. Internet and computer geolocation can be performed by associating a geographic location with the Internet Protocol (IP) address, MAC address, RFID, hardware embedded article/production number, embedded software number (such as UUID, Exif/IPTC/XMP or modern steganography), invoice, WiFi positioning system, or device’s GPS coordinates, or other, perhaps self-disclosed, information. In some IT applications, geolocation usually works by automatically looking up an IP address on a WHOIS service and retrieving the registrant’s physical address.
Location information is needed for several applications in logistics and supply chain management. To sufficiently contextualise the discussion, we first describe in some detail what is implied in this paper by logistics. Outside the supply chain literature, logistics is often conceived and seen in the narrow sense as a resource mobilisation problem, namely, the effective optimisation of processes and resources associated with the deployment or movement of people, goods and services such that project outcomes and end-deliveries are accomplished in good time, to the right locations, in the correct quantity, order and condition, and at optimal cost. It is often assumed that these
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conditions are necessary and sufficient for the total satisfaction of the end customer or beneficiary of the logistics function. In supply chain management, however, there are many other issues that come into play in the matter of logistics. These include adequate information flow within the chain; managerial and resource integration of trading partners that make up the supply chain; security of goods, services and information; efficiency and optimal operation of production and distribution systems; and the inevitable demand and supply uncertainties. However, it would appear judicious to limit this paper to considering logistics as purely a resource distribution problem for which decision support systems are always required (Shim et al., 2002). In this context, this paper examines the roles of both location determination and knowledge of location as inputs in decision support systems for global and local logistics. The main motivation for location determination in logistics is to provide visibility and hence control of processes and resources, and it transpires that process and resource visibility is an essential enabler of efficiency and security in local and global production and distribution systems. Visibility of product and asset can yield the benefits of decreased cycle times, complete prevention of handling faults and shrinkages, reduction of non-value-adding activities, and cost-effective product recalls.
Local or indoor logistics refers to the distribution problem within a restricted geography such as a factory shop-floor, a manufacturing plant, a warehouse or distribution centre, a retail store, or a shopping complex. Global or outdoor logistics refers to the orthogonal complement of the restricted geography (i.e., geographies ranging from a city to the whole of the earth’s surface). The location methods and technologies required (and the associated challenges) in these geographies often overlap but are not always the same. One key benefit of using the knowledge of location to effectively automate and manage logistics is the significant reduction in the impact of logistics on the environment, a recurring and important theme of many governments and corporations around the world. This paper focuses on requirements for location and navigation data and on the mechanisms for obtaining the needed information. The paper also briefly considers the links between logistics and location-based services, a growing area of interest to the logistics community.
Irrespective of its construct and the intrinsic industry, the main purpose of logistics is to serve the supply chain. Supply chains come in many forms and shapes. The supply chain is a complex network of trading partners (buyers and suppliers) between the primary supplier and the end customer. The Toyota motor company, for example, has over 200 direct suppliers providing about 2 billion components of 0.15 million kinds per annum. Most of these components have to be tracked and, if recalls happen, traced from the OEMs (original equipment manufacturers) to the final assembly and beyond. Reverse logistics also requires effective tracking and tracing.
In this paper, we view logistics as a physical distribution or transportation problem, ignoring other important aspects of the logistics function, such as demand and supply uncertainties, manufacturing capacity and facility location and relocation, production planning and scheduling, information flow, supply chain governance structures, value generation and regeneration in supply chains, performance measurement metrics, outsourcing, volatilities and fragilities in supply chains, human resource management, the legal environment and mandates, enabling technologies, etc. (van Eijs, 1994; Singh, 2003; Zographos, 2003; Yang, 2007; Williamson, 2008).
Dynamic fleet management and similar transport optimisation problems benefit directly from location systems (in fact, they rely on the knowledge of location), but while
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we shall discuss the relevant location technologies and methodologies, we shall not delve into the operations (or operational) research issues connected with fleet telematics and management. There are dozens of such problems and proposed solutions of transport route optimisation and resource mobilisation (Grasman, 2006; Friesz, 2007; Zeimpekis et al., 2007; Nagy and Salhi, 2007). Suffice it to say here that knowledge of instantaneous location can help to reduce the complexity of transport route optimisation problems by replacing batch optimisation by one that uses dynamic vehicle location data.
Logistics is a strategic imperative for firms, especially for multinational corporations (Muckstadt and Roundy, 1993). The logistics costs of pharmaceuticals, manufacturing, and merchandising are roughly 5%, 15%, and 26%, respectively. Transportation in the UK, for example, accounts for more than 8% of GDP. In the USA, given the wide geographic extent of the country and the size of its economy, the figure is much higher, approaching almost 12% (Waters, 2003). These figures demonstrate the importance of minimising logistics costs in supply chain operations. Because of globalisation, just-in-time production and distribution, electronic commerce, the continuing growth in bundled logistics services, etc., an efficient distribution and logistics function is now recognised as the new competitive advantage and a strategic prerequisite for the success of firms.
In supply chain and production logistics, the availability of location information can help in reducing inefficient search activities and their negative impact on overall process performance. Moreover, location information also provides a basis for innovative dispatching heuristics that further optimise the scheduling of production runs with respect to various performance indicators, e.g., mean cycle time, asset utilisation, etc. Production flexibility in the form of the ability to coordinate and to control a broad number of process parameters at low costs is another important strategic advantage in a highly competitive and customer driven market.
The location methods and technologies for global (outdoor) and local (indoor) logistics do overlap in many ways but they are not always the same. For reasons of signal availability, attenuation or outright non-availability, severe multipath effects, and achievable or required accuracies, some of the technologies are suitable only in one of the operational domains (outdoor or indoor). GNSS is the natural choice for providing position and navigation data in logistics, but signal outages do occur even in outdoor environments, especially in urban canyons, underground parking lots, and under tunnels. Operational requirements usually necessitate the combination of the available technologies in a specific form of sensor fusion. In the following sections we discuss the location requirements in logistics and the localisation schemes available for meeting the requirements in outdoor/global and indoor/local logistics.
This paper documents the location requirements in logistics from ‘Delphi surveys’ of logistics and navigation industry experts and companies in the UK and USA. The paper is organised as follows. The meaning of ‘Delphi surveys’ is given in Section 2.2. Section 2 presents the motivation for the research and the method of its implementation. Section 3 presents and discusses the results of the research, namely, the requirements for location in various application domains of logistics (land transport, civil aviation, marine navigation, and indoors). Section 4 discusses the technologies that currently exist to meet the location requirements identified in Section 3. Section 5 concludes the paper. Some of the issues for future research are described in Section 5.1. The Appendix contains the list of abbreviations and their meaning.
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1.1 Specific benefits of location and visibility in logistics
To underscore the importance of location data in logistics, Table 1 lists some particular benefits of location, communication and visibility in logistics. All the examples shown in the table contribute to the overall logistics system efficiency and to reducing the impact of logistics on the environment. Table 1 Some specific advantages of location and visibility in logistics
Benefit Note
Improved tractor and trailer deployment
Remote asset-management technologies enable better use of existing tractors and trailers by increasing utilisation frequency, maximising loads, and reducing excess detention. These lower the costs of buying and leasing the vehicles. CSMG (2009) suggested that the average trucking customer is able to boost trailer utilisation by as much as 5% per annum, thereby reducing new trailer purchases by an average of 20% per annum over a five year period and reducing the number of trailers rented every year by up to 60%.
Fuel savings Fuel price hikes is one of the major challenges faced by logistics outfits as they add to pressures on their profits. On average, by deploying mobile asset management technologies, trucking companies can achieve fuel cost reductions on the order of US$500 to US$1,000 per tractor per year. CSMG (2009) reports a case study in which a truckload haulier operating 3,000 trailers and 1,100 tractors achieved a significant return on investment in less than a year by achieving a US$1.9 million reduction in capital spending and US$1.8 million saving in operating costs within the same period. CSMG (2009) suggested that adopters of mobile asset management technologies could realise superior performance in takings per share, by up to 25% per year as compared with the accomplishment of companies that are yet to deploy those technologies.
Agile delivery schedules and rescheduling enabled by end-to-end visibility
Status and progress reports on the movement of goods in transit keep all supply chain players informed of delays and changed delivery schedules. The value of such information increases when deliveries have to be rescheduled on short notice, since loading and unloading often have specific and limited windows. Knowledge of instantaneous location of vehicles facilitates the reduction of the complexity of vehicle routing by replacing batch optimisation by one that calls dynamic vehicle location data.
Efficient fleet management
Firms can achieve lean logistics through increased asset utilisation, right-sizing of fleets and manpower and optimised real-time vehicle routing, especially for load consolidation via merge-in-transit and cross-docking. Also, analysis of accumulated vehicle data (e.g., onboard tachograph measurements) could yield clues on vehicle and driver performance and techniques to reduce fuel consumption in subsequent operations.
Supply chain performance measurement
By analysing the passage of goods and services through the numerous stages of the supply chain, managers could garner new insights into supply chain performance challenges, e.g., by understanding the dynamics of flow of goods and services and addressing the bottlenecks. The UK’s Royal Mail has been experimenting with tracking mails for mail delivery process mapping and evaluation, with possible benefit of efficiency gains (RFID Journal, 2005).
Remote monitoring, control and command
The onboard memory and CPU capacities of sensors monitoring mobile and fixed assets are increasing on a continual basis, just as the footprints of the devices are falling as a result of improved miniaturisation, and prices are falling due to use of low-cost materials and savings from technology integration. Remote sensors are used to collect operational data, monitor cargo while in transit, and identify problems, thereby increasing process flow efficiency and reducing the possibility of losses to the industry.
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Table 1 Some specific advantages of location and visibility in logistics (continued)
Benefit Note
Security and safety
High-valued cargos receive better proactive and reactive protection from real-time and driver-independent monitoring of vehicle movement and vehicle status. The movement of hazardous materials can be better monitored to enforce route compliance, avoid theft, and ensure public safety and security. Also, location-based systems have been developed for pinpointing stolen vehicles, limiting the rate at which fuel flows to their engines, gradually slowing down a stolen vehicle and avoiding potentially dangerous high-speed chase. An example of such a system is OnStar’s Stolen Vehicle Location Assistance. OnStar Corporation is a subsidiary of General Motors that provides subscription-based in-vehicle communications, security, hands-free calling, navigation, and remote diagnostics systems throughout the USA, Canada and China. In Latin American markets a similar service is known as ChevyStar.
Aerial and marine navigation
In both civil and military air and marine navigation, the needs for accuracy in position have increased dramatically in the past decade, particularly for port approach and departure. Not only are accurate positions of crafts now required with full reliability and availability, but new ADS-B (automatic dependent surveillance broadcast) systems (Figure 2) allow craft’s transponders to transmit their locations, along with other pertinent data from the craft’s flight monitoring system, to other crafts and ground stations equipped to receive the information. ADS-B ground stations tot up radar-based targets for non-ADS-B-equipped crafts to the mix and telemeter all the information back up to equipped crafts, together with information on weather and flight restrictions.
Military logistics
The mobilisation, deployment, operation, command and control of forces in the modern war theatre all require advanced logistics and localisation. Location and navigation are playing ever greater roles in military operations. Perhaps less well known is the use of localisation in autonomous land, air and sea vehicle navigation, and in ad hoc sensor networks that may be quickly deployed for covert and live operations. The Defence Advanced Research Projects Agency (DARPA) of the USA has a goal that between 2015 and 2020, 30% of its combat operations (particularly resupply operations) will be undertaken by unmanned, robotic vehicles. This has necessitated research into sophisticated real-time mobile mapping and localisation technologies in recent years (Finkelstein, 2009).
Emergency and rescue operations
Emergencies are classical logistical nightmares. They are intrinsically hard to effectively plan and prepare for, since they are usually so diverse in nature and characteristics and in such circumstances the critical infrastructure that is relied upon for normal operations is often compromised and backup systems are usually costly or impossible to install in short time scales, when the ambient environment is already challenged. Nevertheless, the availability of real-time location information about the resources that are needed to tackle emergencies can enhance mission success. Indeed modern emergency and rescue missions (irrespective of their nature) depend critically on the availability of such information. The ready availability of dynamic location information reduces the complexity of the task. Autonomous advanced decision support systems are currently being developed which will permit the virtual deployment of assets in various emergencies (Jones, 2006). Such systems assume as given the availability of real-time information about the locations and physical states of all documented assets.
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Table 1 Some specific advantages of location and visibility in logistics (continued)
Benefit Note Travel directions, mobile commerce, mobile advertisement, and other services based on location
These are a hotchpotch of commercial applications of location that are sometimes referred to as ‘location-based services’. They range from accessing travel directions from an onboard personal navigation device (standalone or connected via the mobile internet); getting information about traffic conditions, weather, gas price, etc., over the air (e.g., the US Department of Transportation’s SafeTrip-21 initiative); address sharing; salesmanship; roadside emergency services; locating services and facilities (e.g., restaurants, hospitals, fire hydrants) in cities; locating family, friends and pets outdoors; pay-as-you-use road toll charging; thoroughfare access control; location-based access to media such as fixed and mobile radio and TV; tracking and tracing of animals and supplies through the food chain; mobile advertisement; geotagging of pictures and audiovisual materials and products; to many more services, some of which are still emerging. For example, location enables more targeted, less intrusive, and genuinely useful information to be transmitted to a location service user. It also enables service providers to authenticate service users and restrict access to services: for instance, services may be provided or denied depending on the location of the requester (Azvine et al., 2005; Ahson and Ilyas, 2011). The FCC (the US Federal Communications Commission) prevailing mandates set requirements for mobile-originated emergency calls to be positioned in the USA so that the mobile terminal can be located to an accuracy of between 50 and 300 m for 67 to 95% of calls, depending on the location technology used (network-based or mobile terminal-based) and the location of the caller. Such requirements are fulfilled by mobile operators by using the mobile station (MS)-assisted version of Assisted-GPS (AGPS).
2 Research motivation and methodology
2.1 Motivation
Location information is needed for most activities in logistics, including: urban and national transportation, vehicle dispatch, fleet management, distribution management, retail outlet siting, customer segmentation and analysis, and dispatch rules on factory shop-floors. The chief application of location in logistics is to provide a mechanism for attaining some level of visibility (concurrent or historical) of mobile assets, inventory and personnel so as to plan, execute and monitor the effective and efficient deployment of the resources. In modern logistics, given the dynamic nature and complexity of business activities, off-shoring, outsourcing, constant change, etc., there is much priority accorded to real-time information and synchronised decision making. Real-time location information provides supply chain operators and managers (shippers, carriers, forwarders, hauliers, logistics service providers, terminal operators, retailers, and solution providers) with one important input of decision support systems for real-time inventory management, security management, and strategic decision making based on reality. Visibility and control in supply chain management are realised by location, physical condition-monitoring and mobile communication technologies. However, this paper focuses on only location, the enabling location technologies, and the standards of accuracy, integrity and availability with which location data are needed and can be provided in logistics.
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Given the well-recognised potential and widespread application of location technologies in logistics (Table 1), one might be forgiven to assume that location requirements (in terms of the needed availability and accuracy) for various application scenarios in logistics have been comprehensively analysed, determined and documented. This, unfortunately, is not the case, and in view of the myriad of application domains and circumstances, the task is not as straightforward as it might first seem. The requirements indicated in this paper are based on an extensive review of both logistics and location technology literature, and on Delphi studies involving industry experts (logistics service providers, location technology providers and vendors, and academics). In discussing the location requirements in logistics, we would concentrate on the following application scenarios and assume that location information would be required to be available 90 to 95% of the time and at 95% confidence level (i.e., two-sigma level), with relatively high needs on solution integrity (i.e., quality), continuity, and system availability.
Availability is defined both in space and time as the relative percentage of a given testing interval (space or time) during which a navigation device has a valid position fix. Validity of a position fix is indeed determined by user requirements. For instance, in the case of positioning by GNSS signals, erroneous signal generation (which may originate from a combination of digital and analogue errors), deliberate spoofing, and signal multipath effects all affect satellite navigation signal quality, thus necessitating signal authentication and integrity monitoring schemes. Navigation message authentication schemes usually are based on a well-designed message structure, together with a signing and verification procedure often derived from the Rivest-Shamir-Adleman public-key algorithm in the Digital Signal Standard (RSA-DSS). The most popular integrity assertion techniques are to be found within the receiver autonomous integrity monitoring (RAIM) schemes, where snapshot procedures are common. RAIM consists of monitoring, independently at each instant of time, the projection of the error vector with the help of the least squares method of estimation (Hewitson and Wang, 2010).
Figure 1 lists some various local and global application scenarios in which location information is required in logistics. ‘Local’ here refers to applications of location and navigation data in confined or restricted spaces such as factory shop-floors, shopping complexes, and sport arenas; ‘global’ functions, in principle, have no geographic limits. Figure 1 was used as a framework to develop the Delphi questionnaire and for presenting the results in Section 3.
However, while the needs are well-known, the requisite standards and accuracies with which location information should be provided and the means to meet them (i.e., to achieve the standards and accuracies) are not documented in the logistics literature. The aim of this paper is therefore to fill these gaps. To address these issues, as a first step, empirical data was generated from the judgements and views of industry experts and analysed to arrive at the requirements for location in logistics. Analytical-cum-simulation approaches will have to consider potentially hundreds of variables and dozens of use scenarios of location in logistics and may hence be intractable for realistic cases. The judgements of the industry experts take into account their experiences and are thus, in this case, more of practical utility than theoretically derived results. The empirical methods adopted and the results reported in this paper can be used to guide simulation studies and reduce the complexity of such studies.
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Figure 1 Typical application scenarios requiring location information in local and global logistics
2.2 Methodology
This paper reports the results of a Delphi study of the judgements and views of the logistics, navigation, automobile, and semiconductor industry experts and companies in the UK and USA on requirements for location in logistics. The nature of the Delphi method actually used in this paper is detailed later in this section. Originally, 400 potential participants/panellists were selected through stratified sampling from various industry databases. Of the nominated 400 panellists, only 168 actually agreed to participate in the study, giving a participation rate of nearly 42%. Table 2 presents the industries of the panellists in the Delphi study, and Table 3 gives the sizes of the companies of the panellists according to the numbers of their employees. The choice of the threshold 400 was based purely on the assumption that given the experience of the potential panellists (none of them had less than ten years’ experience in their industry) and the spread of their industries, the information they provided would sufficiently reflect the needs of the logistics industry. The panellists included both industry practitioners and academics from universities. For the majority of the industrial sectors, the number of panellists who are academics was roughly half that of practitioners. The reason for the disparity between the number of practitioner and academic panellists was that this study was more focussed on practical needs than theoretical niceties. The Delphi panel discussion was conducted by e-mails between January and April 2012.
The first stage of this study was to conduct a systematic and extensive review of the existing multi-disciplinary literature on logistics, location determination, and navigation. The second stage of the study involved using a special form of the Delphi method as follows. The Delphi procedure we used consisted of preliminary inquiries sent to the potential panellists to obtain their willingness to participate in the project and provide solicited information. This was then followed by ‘Delphi by correspondence’. The questions in the questionnaire were cast to prompt adequate and fast responses from the panellists.
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The Delphi method is a structured, systematic, interactive forecasting technique that relies on the views and judgements of a panel of experts. The experts answer questions in two or more rounds. After each round, a facilitator provides an anonymous summary of the experts’ opinions and forecasts from the previous round as well as the reasons they suggested for their judgements. Thus, experts are supported to revise their earlier answers in light of the responses of other panellists. It is assumed that during this process the range of the answers will shrink and the group will converge towards the ‘correct’ answer. The process is terminated after a pre-defined exit condition (e.g., number of rounds, achievement of consensus, stability of results) and the mean or median scores of the final rounds determine the results. The Delphi method is based on the principle that decisions from a structured group of individuals are more accurate than those from unstructured groups.
The traditional approach to holding a Delphi is that the panellists sit in the same venue and speak with each other in a controlled discussion. Instead of adopting this expensive routine of collecting all the panellists in the same venue, our special Delphi study was conducted by e-mail exchanges between the researchers (as facilitators or coordinators) and the panellists. This new Delphi scheme also allowed us to significantly increase the number of panellists – and hence increase the robustness of the outcome – beyond what would ordinarily have been possible by the conventional Delphi method. The panellists did not know each other, thereby removing any bias that might have been triggered otherwise. After each round of receiving responses from the panellists, outlier values of requirements for location were computed from the given responses to each requirement using the outlier detection scheme of Grubbs (1969). The outlier values and the mean of the responses to each question were then e-mailed to all the panellists whose answers happened to be outliers, with a request to them to either revise or defend their submitted values. If the panellist whose value of a requirement happened to be an outlier insisted on the validity of his or her entry rather than revise the value, such a value was then omitted from computing the current mean of the requirement. This happened in only about 2% of the questions in the questionnaire. In a few cases a panellist who has submitted an outlier value would ask for some explanation of the reasons given by the other panellists for their non-outlier values. In such a situation we would ask at least five of the panellists who have given non-outlier values to offer explanations for their judgements. We would then e-mail the explanations to the panellist who has specified an outlier value and has requested such accounts.
The opportunity given to the panellists to revise their discordant entries based on the views of other panellists is a more standard and powerful approach in empirical research than simply rejecting outlier responses. In fact, some of the techniques used for outlier detection and management (Hodge and Austin, 2004) resemble the Delphi process. For applications of location that the Delphi panellists provided no data, we used the requirements found in the literature. In such cases, the sources of the information are specifically indicated in Section 3. After the requirements were determined, we then carried out another extensive literature review, this time to map out the methods and technologies that are available to meet the current needs. Table 8 represents the outcome of this second literature review.
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Table 2 Industrial sectors of panellists
No. of panellists Industrial sector
Practitioners Academics Supply chain management 25 16 Logistics services 21 10 Aviation 12 5 Road transport 13 5 Rail transport 8 4 Marine transport 9 5 Geolocation services 6 3 Geolocation equipment manufacturers 4 0 Automotive OEMs and assemblers 5 0 IT/IS products and services 11 6 Subtotal 114 54 Total 168
Table 3 Sizes of companies by number of employees
Number of employees Percentage
1–50 50 51–250 20 251–500 17 501 and above 13 Total 100
3 Results and analysis
3.1 Location requirements in transportation
Location requirements in transportation vary remarkably within the various transportation modes, environments and applications (land transport, urban transport, sea transport, inland water navigation, air transport, aircraft approach and landing, seaport and airport operations, etc.), such that it is not feasible to review the needs of all the modes in this paper. However, it can readily be asserted that the most stringent requirements come from: aircraft landing; rail track matching with moving trains – the minimum centreline-to-centreline spacing between parallel tracks is 3.5 m (Simsky et al., 2004); urban transport (especially in urban public transport, route guidance, emergency and rescue operations, and commercial fleet management); and container port operations.
In container port/terminal operations, centimetre-level accuracy is needed for controlling automated guided vehicles (AGVs) and automated lift vehicles (ALVs), and for operating rubber-tired gantry cranes, rail-mounted cranes, straddle carriers, etc., for container movement and stacking. The location challenges in container stacking and in other quay and container yard operations are well-highlighted by Kim et al. (2003) and van Hees (2007).
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Table 4 Land transportation location and navigation data accuracy requirements
Transport mode Accuracy (meters, 95%)
Highways Navigation and route guidance 2.5–10 Automated vehicle monitoring 25 Automated vehicle identification 25 Public safety 5–10 Resource management 30 Accident and emergency response 30 Collision avoidance 0.5–1 Transit Autonomous vehicle control 10–30 Automated voice bus-stop announcement 5 Emergency response and rescue 70–100 Transportation data collection 5 Rail Train and rail vehicle control 1 Tract defect detection and identification 0.3 Asset mapping 0.1 Emergency response and rescue 60–100 Bridge construction and bridge monitoring 0.001
Note: Some of the numerals have been rounded to whole numbers.
3.1.1 Land transportation
In land transportation the user services requiring location and navigation information include: travel and transportation management (pre-trip planning, en route driver information, route guidance, incident management, travel demand planning and management); public transport operations (public transportation management, personalised public transportation); commercial vehicle operations (advanced and automated commercial fleet planning and management); emergency and rescue operations (emergency vehicle dispatch and management, emergency alert and personal safety); and advanced vehicle control and safety systems (in-vehicle signing for situation awareness). Table 4 provides the land transportation location and navigation data accuracy requirements as deduced from the Delphi study. The requirements reflect absolute position (as opposed to relative position) needs and include those needed for infrastructure development.
3.1.2 Fleet telematics and management
In fleet telematics and commercial transportation management, the needed horizontal location accuracy is usually in the range of 1 to 40 m at two-sigma level with high integrity, continuity and availability. These results correlate with those suggested by Goel (2008), Quddus (2006), and Quddus et al. (2006a, 2006b). Typically, bus priority
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provisioning requires a two-dimensional location accuracy of 5 m at two-sigma level with guaranteed integrity. In urban areas, this accuracy is often difficult to attain to the requisite availability standards even with the incorporation of supplementary data sources (such as assisted GPS and feature/map aiding) into conventional GNSS and INS location algorithms.
3.1.3 Vehicle infrastructure integration
In the framework of the nascent concepts of vehicle infrastructure integration (VII) and associated dedicated short-range communication (DSRC) for transportation management (Bilchev et al., 2004), location requirements are much higher. This is usually because of the need to precisely identify the lane on which a vehicle is cruising in close proximity to other vehicles, together with the need to know its speed, acceleration and heading. IVS for situation awareness (i.e., bringing road signs, warnings and traffic conditions to the attention of the driver via onboard electronic interfaces) also places stringent requirements on the location of vehicles. V2X is the group of common underlying system components of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. V2X and advanced driver assistance system (ADAS) applications differ remarkably in their position accuracy and availability needs. Forward collision warning (FCW) and lane change advisor (LCA) require higher accuracy in location because they must identify same-lane vehicles. Emergency electronic brake light (EEBL), on the other hand, only requires identification of same-road vehicles. Some ADAS applications require accurate location information in an absolute sense, while others may function with location information that is only relatively accurate. Almost all V2V features may function properly with only relative location information (relative location and orientation of surrounding vehicles). However, almost all V2I applications need absolute location information so as to adequately orient roadway features with vehicle location.
The Research and Development Unit of General Motors uses the following classification of location information accuracy needs (Basnayake, 2009): which road, better than 5 m; which lane, better than 1.5 m; and where in lane, better than 1 m. This classification is used in expressing both relative and absolute accuracies. See also Bilchev et al. (2004), Warren (2004), and Hsu et al. (2007).
3.2 Location requirements in civil aviation
In the case of aircraft navigation and guidance, we shall focus on only civil aviation requirements. In fact, tactical air operations have important similarities to civil aviation in the four phases of en route (including oceanic and remote areas), terminal, approach and landing, and surface. The main differences between tactical air operations and civil aviation are in the areas of air manoeuvrability, speed, payload discharge and guidance, and precautions for crew safety in enemy territory.
Rather than ask the Delphi panellists to suggest the necessary accuracy, reliability and availability requirements for civil aviation, we asked them to confirm their agreement or otherwise with the ICAO standards for Navigation Service Levels APV-I, APV-II, CAT-I, CAT-II and CAT-III, all of which are described below. We also requested them to confirm their agreement or otherwise with the contents of Tables 5 and 6, both of which relate to Automatic Dependent Surveillance Broadcasts (ADS-B). All the Delphi panellists responded that, given the adoption process of the ICAO standards and
An assessment of location data requirements in logistics 187
recommended practices, it was not necessary or sensible for anyone to suggest changes to the standards and recommended practices outside ICAO. Hence, the results reported in this section pertain only to ICAO standards and recommendations.
Standards and Recommended Practices (SARPs) are technical specifications adopted by the Council of ICAO in line with Article 37 of the Convention on International Civil Aviation (CICA) so as to attain the highest possible level and extent of uniformity in regulations, standards, procedures and structure regarding aircraft, personnel, airways and auxiliary services to simplify and enhance air navigation. SARPs are published by ICAO in the form of Annexes to the Chicago Convention. Annexes are not international treaties and hence SARPs lack the same legal weight as the Chicago Convention. Additionally, States agreed to “undertake to collaborate in securing ... uniformity”, not to “comply with” SARPs. Each Contracting State is entitled to advise the ICAO Council of differences between SARPs and its own local regulations and practices. Those differentiations are published in the form of Supplements to Annexes.
A standard is defined by ICAO as “any specification for physical characteristics, configuration, material, performance, personnel or procedure, the uniform application of which is recognized as necessary for the safety or regularity of international air navigation and to which Contracting States will conform in accordance with the Convention” (Milde, 2008). A Recommended Practice is defined by ICAO as “any specification for physical characteristics, configuration, material, performance, personnel or procedure, the uniform application of which is recognized as desirable in the interest of safety, regularity or efficiency of international air navigation and to which Contracting States will endeavour to conform in accordance with the Convention”. ICAO authenticates observance of SARPs through audits of State’s oversight structures and practices. Universal Safety Oversight Audit Programme (USOAP) and Universal Security Audit Programme (USAP) are the two extant audit schemes.
Unaided GPS is capable of providing aerial navigation (RNAV) en route and terminal navigation to position an aircraft in the vicinity of an airport. For landing, the aircraft electronics system switches to approach navigation. Approaches are classified as either ‘precision’ or ‘non-precision’, depending on the accuracy and functionalities of the available navigational aids. Precision approaches use both lateral (course) and vertical (glide slope) guidance to a decision height. If the needed visual references, such as the approach lights or the runway environment, are not in good view at this height, the pilot must fly a ‘missed approach’, which is a prescribed, controlled routing away from the runway.
Non-precision approaches provide lateral (course) guidance only, using a ‘minimum descent height’. This height is defined as the height below which an aircraft must not descend until and unless visual reference has been confirmed. It is typically between 75 m (250 ft) and 150 m (500 ft), depending on the airport in question. Unaided GPS is capable of providing non-precision approach, commonly referred to as lateral navigation (LNAV). On an LNAV approach, the pilot flies the final approach using lateral guidance, but when the aircraft reaches the final approach threshold, the pilot descends to a minimum descent height using the barometric altimeter. Satellite-based augmentation systems (SBAS) provide the additional capability for the aircraft to use GPS for vertical navigation (VNAV). LNAV/VNAV is an approach in which a vertical glide slope guides the aircraft to a distance of about 1,140 m (3,800 ft) before the runway limit, which is at an average decision height of 105 m (350 ft).
188 A. Musa et al.
ICAO specifies performance requirements for navigation services in the aviation industry. These requirements are defined as Navigation Service Levels APV-I, APV-II, and CAT-I. Approach Procedures with Vertical Guidance (APV) are instrument approach Navigation Service Levels that use horizontal (LNAV) and vertical (VNAV) navigation guidance but they do not meet the requirements for precision approach and landing operations. APV is also referred to as LPV (Localizer Performance with Vertical Guidance). An APV approach uses lateral guidance from SBAS and vertical guidance provided by either the barometric altimeter, GBAS or SBAS. In North America, an APV approach enables descent to 60 to 75 m (200 to 250 ft) above the runway, and can only be flown with a WAAS-enabled receiver certified by the US Federal Aviation Authority (FAA).
Two types of APV are specified: APV-I and APV-II. APV-II has stricter requirements, such as 8 m vertical accuracy 95% of the time, compared with 20 m accuracy for APV-I. SBAS using geostationary satellites permit equipment users to fly APV approaches based on the procedure and design criteria published by the ICAO Obstacle Clearance Panel. Category I (CAT-I) is a precision approach Navigation Service Level for the final approach phase of flight with higher requirements for accuracy, integrity, availability, and continuity than APV-I and APV-II. The vertical accuracy in this category is between 4 and 6 m. Presently, in Europe the Instrument Landing System (ILS) Category I approach is the only certified navigation system that supports CAT-I processes.
An extension of the Standard and Recommended Practices (SARPs) to enable use of ground-based augmentation systems (GBAS) to support operations to CAT-II/III minima also exists. CAT-I is a precision instrument approach and landing with a decision height not lower than 60 m (200 ft) above touchdown zone elevation and with either a visibility not less than 800 m (2,265 ft) or a runway visual range not less than 550 m (1,804 ft). CAT-II is as CAT-I, with a decision height lower than 60 m (200 ft) but not lower than 30 m (100 ft), and a runway visual range not less than 350 m (1,148 ft). CAT-IIIA is a precision instrument approach and landing that stipulates a decision height lower than 30 m (100 ft) or no decision height, and a runway visual range not less than 200 m (656 ft). CAT-IIIB – GBAS Approach Service Type D (GAST-D) – specifies a decision height of lower than 15 m (50 ft) or no decision height above touchdown elevation, and a runway visual range less than 200 m (656 ft) but not less than 75 m (246 ft). Autopilot is used until taxi-speed. In the USA, FAA criteria for CAT-IIIB runway visual range allows readings as low as 46 m (150 ft). CAT-IIIC does not specify a decision height or any runway visual limitations. This category is not yet in operation anywhere in the world, as it requires guidance to taxi in conditions of zero visibility. Category IIIB is currently the best available system.
An APV approach with a 200-foot decision height is sometimes called an APV200 (or LPV200) approach. In general, the location equipage aboard an aircraft for APV200 approach has to satisfy very stringent requirements of accuracy, reliability and availability. Of these, reliability is the most important consideration, and this is achieved by creating a very reliable hardware and software solution. The combined hardware and software reliability has to be such that erroneous results occur with a probability less than 10Exp-7 at 100% confidence level during approach and departure. The Minimum Aviation System Performance Standards (MASPS) for GPS Local Area Augmentation System Airborne (LAAS) Equipment (RTCA Do-253C) stipulates several additional integrity requirements or augmentations. These include position solutions with a variety
An assessment of location data requirements in logistics 189
of smoothing time constants – e.g., dual ionospheric gradient monitoring (DSIGM) – fault detection before and during new satellite additions, satellite geometry scrutiny, and optional onboard autonomous integrity monitoring (Bestmann et al., 2010a, 2010b).
Figure 2 Automatic dependent surveillance system
In the context of ADS-B, which is depicted in Figure 2, several parameters have been defined by the standards community. These define distinct location performance levels for ADS-B, including navigation accuracy category for position (NAC-P) and navigation integrity category (NIC), each with 11 sub-categories; and surveillance integrity level (SIL), which is the probability of containment associated with NIC (Xi et al., 2009; Mohleji and Wang, 2010; ICAO, 2012, 2013; FAA, 2010, 2011; Air Services Australia, 2012; RTCA, 2002, 2006). See Table 5. ADS-B is a cooperative surveillance system which uses a data communication protocol with automatic broadcast of identification, position, velocity, and other pertinent parameters by participating users. Equipment capable of receiving the data can use the data for a variety of applications ranging from situational awareness to aircraft separation management. Table 5 lists the ADS-B navigation accuracy and integrity categories, NAC-P and NIC, and their respective performance requirements. The estimated position uncertainty (EPU) is a 95% accuracy of the position fix, and the containment radius (CR) is a bound on the position error associated with the integrity level represented by SIL. SIL levels range from zero to three, representing unknown integrity levels 10Exp-3, 10Exp-5, and 10Exp-7, respectively. The entries in Table 5 emanate from RTCA Do-260A (RTCA, 2006), with the exception of NIC 6b, which is an unreal category expressive of the containment need specific to the ATLAS programme.
Table 6 indicates both published and draft location performance requirements expressed in terms of ADS-B categories for various applications ranging from radar-like surveillance in non-radar airspace (NRA) to better-than-radar performance for radar
190 A. Musa et al.
airspace applications (RAD), including parallel approach, Final Approach and Runway Occupancy Awareness (FAROA), and surface applications such as Airport Surface Situational Awareness (ASSA). It is to be noticed that most of the applications require a SIL level of 2 (or better), which corresponds to containment integrity of 10Exp-5 per hour; existing certified RAIM algorithms provide 10Exp-7 per hour, corresponding to a SIL level of 3 (Harris and Murphy, 2008). Table 5 ADS-B accuracy and integrity categories together with related performance
requirements
95% horizontal and vertical accuracy bounds (EPU and VEPU)
Horizontal and vertical containment bounds NAC-P
EPU VEPU
NIC
CR VCR 0 = 18.52 km 0 = 37.04 km 1 < 18.52 km 1 < 37.04 km 2 < 7.408 km 2 < 14.816 km 3 < 3.704 km 3 < 7.408 km 4 < 1,852 m 4 < 3.704 km 5 < 926 m 5 < 1,852 m 6 < 555.6 m 6 < 1,111.2 m 6b < 555.6 m 6b < 926 m 7 < 185.2 m 7 < 370.4 m 8 < 92.6 m 8 < 185.2 m 9 < 30 m < 45 m 9 < 75 m < 112 m 10 < 10 m < 15 m 10 < 25 m < 37.5 m 11 < 3 m < 4 m 11 < 7.5 m < 11 m
Note: 1 nautical mile (nm) = 1,852 metres (m).
Table 6 Application-dependent ADS-B accuracy and integrity requirements
Application NAC-P NIC SIL NRA (9.26 km) Do-303 5 4 2 NRA (5.56 km) Do-303 6 5 2 NRA CASCADE Do-260A 5 4 3 NRA CASCADE Do-260 5 5 3 NRA ATLAS Australia 0 6b 2 NRA NPRM FAA 9 7 2 NPRM Comment Boeing 8 6 2 RAD en route (9.26 km) Draft 7 5 3 RAD Terminal (5.56 km) Draft 8 6 3 RAD dep. par. appr. (4.63 km) Draft 8 7 3 RAD ind. par. appr. Draft 8 7 3 En route vis. sep. appr. RFG draft 6 6 1 ASSA/FAROA Surface Draft 9 0 0 In trail procedures RFG Do-312 5 5 2
An assessment of location data requirements in logistics 191
3.3 Location requirements in marine navigation
Figure 3 lists the major marine navigation phases and application scenarios and Table 7 depicts the marine location and navigation requirements. However, not all the current requirements are shown in Table 7. The stricter requirements emanate from safety of navigation in inland waterways, where a two-dimensional accuracy of 2 to 5 m is normally needed at the 95% confidence level (two-sigma). For harbour approach and entrance, the required accuracy is normally 8 to 20 m (95% confidence level). On the high seas the accuracy requirements are less severe, but the need to avoid protected or endangered marine areas and dangerous weather and sea conditions (wrecks, rocks, and reefs for Scuba diving and fishing) demands accuracies of the order of 100 m. Horizontal positional accuracies of 1 to 100 m (at 95% confidence level) are needed for resource exploration and search and rescue operations in coastal waters. Table 7 Marine location and navigation data requirements
Requirements Accuracy (m)
Availability (%)
Fix interval (seconds)
Fix dimensions
Inland waterways
All ships and tow boats 2–5 99.9 1–2 2
Recreational boats and smaller vessels 5–10 99.9 5–10 2
River engineering and construction vessels 0.1–5 * 99.0 1–2 3
Harbour entrance and approach
Large ships and tow boats 10–20† 99.7 6–10 2
Smaller ships 5–20 99.9 † 2
Resource exploration 1–5 99.0 1 2
Engineering and construction vessels 0.1–5* 99.0 1–2 2 and 3
Fishing, recreational and other small vessels 8–20 99.7 † 2
Coastal navigation
All ships and tows 460 99.7 120 2
Recreational boats and smaller vessels 460–3,700 99.0 300 2
Commercial fishing 460 99.0 60 2
Resource exploration 1.0–100 99.0 1 2
Search operations and border enforcement 460 99.7 60 2
Recreational fishing 460 99.0 300 2
Ocean navigation
All ships 1,800–3,700 99.0‡ ¶ 2
Large ships 185–460 99.0 300 2
Resource exploration 10–100 99.0 60 2
Search and rescue operations 185–460 99.0 60 2
Notes: *Vertical dimension; †harbour dependent; ‡at least every 12 hours; ¶ 15 minutes minimum or as desired, 2 hours maximum.
192 A. Musa et al.
Figure 3 Marine navigation application scenarios
All these requirements are met by differential GPS (DGPS), which normally provides a two-dimensional error of 1 to 3 m, greatly enhancing harbour entrance and navigation. Marine DGPS currently supports a wide range of public, private and military needs, ranging from hydrographic surveying, rig installation and functions, vessel traffic management services, search and rescue operations, environmental assessment and clean-up operations, to underwater mine detection and disposal. For example, the US Coast Guard Maritime DGPS Service broadcasts correction signals on marine radio-beacon frequencies to improve the accuracy and integrity of GPS-derived positions. The service provides 10 m accuracy (at 95% confidence level), together with integrity alarms for GPS and DGPS out-of-tolerance conditions within 10 seconds of detection. The service availability is 99.7% per month.
As a result of the success of DGPS and budgetary constraints, the US Coast Guard terminated the transmission of Loran-C signals in February 2010, but an enhanced Loran system (eLoran) is currently in operation in, e.g., UK, operated by VT Communication on behalf of the General Light House Authorities of the UK and Ireland. eLoran is independent of, dissimilar to, and complements GNSS. It meets the accuracy, availability, integrity, and continuity performance required for aviation, non-precision instrument approaches, maritime harbour entrance and approach, and many aspects of location-based services (LBS). It is also a precise source of frequency for many applications, including telecommunications. A GPS jamming test conducted by the General Light House Authorities of the UK and Ireland in 2009 (Basker et al., 2010; Bartone, 2008) concluded that the denial of GPS and DGPS services has a significant impact on maritime safety. eLoran was unaffected by GPS jamming and achieved an accuracy of 8.1 m (95% confidence level), which is comparable to standalone, single-frequency GPS.
An assessment of location data requirements in logistics 193
3.4 Indoor requirements
The provision of navigation and guidance services in challenging indoor environments (in manufacturing complexes, large office buildings, shopping centres, warehouses, railway stations, airports, hospitals, sport arenas, etc.) is very important. In the case of emergency services (e.g., to locate first responders in a rescue situation inside a building), obtaining reliable location is particularly very challenging and superior location accuracies and reliabilities are required than in outdoor environments. The panellists agreed that most applications of indoor positioning demand that a user can be located in a certain room inside the building. As a consequence, the required location accuracy depends on the typical room size in the application domain. For example, in the case of a shopping complex, an accuracy of better than 3 m for location determination in the two-dimensional horizontal plane is necessary. In addition, the system must be able to locate the user on the correct floor in a multi-storey building. The accuracy requirement in height, therefore, depends on the ceiling height of the building: typically, the required accuracy would be better than 2.5 m for modern office buildings.
The most stringent accuracy requirements in indoor logistics are found in some manufacturing shop-floors and in scientific research laboratories. In complex manufacturing scheduling processes, such as in semiconductor fabrication labs (Thiesse and Fleisch, 2008), the needed positional accuracy may exceed 10 cm in the horizontal plane (Baker, 1998; Kuo and Huang, 2006; Dabbas and Fowler, 2003). There is a large number of activities and movements of parts that take place in a typical semiconductor lab and there is complexity in providing location information for process automation in those facilities.
4 Location technologies and methods for logistics
Having determined the requirements for location data for logistics in the foregoing sections, in the present section we chart, through a second round of extensive literature review, the array of positioning technologies that can deliver the needs. As mentioned earlier, location technologies for logistics can be categorised according to the environment (indoors or outdoors) in which they are most suitable. However, many of the methods are usually combined through hybridisation of sensors in the two environments of indoor and outdoor. Table 8 presents the many methods that are at the core of location for logistics applications indoors and outdoors. Figure 4 depicts the system architecture of assisted-GPS. Some of the older and waning methods have not been included in the table simply because their future is greatly diminished.
The use of high-sensitivity GNSS (HS-GNSS) receivers is not included in Table 8. HS-GNSS receivers acquire and track weak signals by lowering the threshold of useful signal-to-noise ratio, whereby the sensitivity can be increased by as much as 15 dB compared with a conventional receiver. Signal availability thus increases by using a HS-GNSS receiver, but nonetheless the corresponding noise and errors are also inadvertently magnified (Ziedan, 2006). Furthermore, a HS-GNSS receiver requires more accurate time reference and is likely to consume slightly more power during acquisition than equivalent non-HS-GNSS receiver. HS-GNSS for indoor applications, whilst still important and attractive, seems to be giving way to the use of radio signals from
194 A. Musa et al.
preinstalled infrastructure (like WiFi, UWB, RFID, IMES), and use of digital map databases of interiors. For example, in the UK there are some small enterprises that specialise in gathering precise databases of interiors of public and private buildings for emergency needs by first responders. Table 8 Taxonomy of modern location technologies with logistics potential
Tech
nolo
gy
Indo
or
appl
icab
ility
O
utdo
or
appl
icab
ility
R
elat
ive
mer
its
Rel
ativ
e de
mer
its
Typi
cal i
ndoo
r an
d ou
tdoo
r ac
cura
cy (9
5%)
GN
SS-b
ased
met
hods
Pure
, una
ided
GN
SS
App
licab
le b
ut th
e ou
tcom
e is
usu
ally
un
relia
ble.
App
licab
le.
Free
and
ava
ilabl
e
alm
ost e
very
whe
re, e
xcep
t in
door
s an
d un
derg
roun
d.
Sign
als
cann
ot p
enet
rate
mos
t bui
ldin
gs w
ith
suff
icie
nt s
tren
gth
for t
he re
ceiv
er to
dec
ode
the
navi
gatio
n m
essa
ge. S
ever
e m
ultip
ath
effe
cts
occu
r ind
oors
and
in u
rban
are
as.
Tre
e ca
nopi
es c
an im
pair
sig
nal r
ecep
tion.
30 to
100
m, d
epen
ding
on
obse
rvat
ion
mod
e. P
PP c
an
do m
uch
bette
r, yi
eldi
ng
cent
imet
re a
ccur
acy.
Lan
d-ba
sed
DG
NSS
A
pplic
able
if
rece
iver
can
als
o de
code
GN
SS
sign
als.
App
licab
le.
Incr
ease
s av
aila
bilit
y an
d ac
cura
cy. C
orre
ctio
ns c
an
be s
ent b
y m
any
mea
ns:
mar
ine
radi
o-be
acon
s,
VH
F an
d U
HF
data
link
s,
cell
phon
e ne
twor
ks, r
adio
an
d te
levi
sion
bro
adca
sts,
in
tern
et, a
nd L
oran
sig
nals
.
Req
uire
s pr
eins
talle
d, c
ostly
gro
und-
base
d in
fras
truc
ture
. May
nee
d a
netw
ork
conn
ectio
n an
d su
bscr
iptio
n. T
rack
ing
and
mul
tipat
h er
rors
are
unc
orre
late
d be
twee
n re
fere
nce
and
user
sta
tions
.
1 m
, dep
endi
ng o
n th
e as
sist
ance
dat
a. N
RT
K
achi
eves
bet
ter t
han
5 cm
w
here
refe
renc
e st
atio
ns a
re
dens
e an
d co
rrec
tion
late
ncy
is
low
.
SBA
S an
d si
mila
r A
pplic
able
if
rece
iver
can
als
o de
code
GN
SS
sign
als.
App
licab
le.
Prov
ides
dif
fere
ntia
l co
rrec
tions
and
inte
grity
in
form
atio
n at
aff
orda
ble
cost
(Pra
sad
and
Rug
gier
i, 20
05).
Nor
mal
ly c
over
s a
wid
e ar
ea.
Req
uire
s co
stly
pre
inst
alle
d, s
pace
-bas
ed
and
grou
nd-b
ased
infr
astr
uctu
re. S
igna
l D
oppl
er c
ollis
ions
are
like
ly d
ue to
low
dy
nam
ics
of S
BA
S sa
telli
tes.
Im
prov
ed
ephe
mer
is a
nd c
lock
dat
a br
oadc
asts
by
GN
SS s
atel
lites
them
selv
es p
lus
dual
fr
eque
ncy
iono
sphe
re c
orre
ctio
n fo
r civ
il us
ers
coul
d lim
it th
e be
nefi
ts o
f wid
e-ar
ea
diff
eren
tial G
NSS
.
1 m
AG
PS
App
licab
le.
App
licab
le.
Incr
ease
s av
aila
bilit
y
and
accu
racy
. Red
uces
T
TFF
. Can
als
o se
nd
othe
r ass
ista
nce
data
su
ch a
s na
viga
tion
m
odel
s, re
fere
nce
loca
tion,
fr
eque
ncy,
and
tim
e
(van
Dig
gele
n, 2
009)
.
Nee
ds n
etw
ork
conn
ectio
n, a
nd th
ere
are
priv
acy
conc
erns
. TT
FF is
not
alw
ays
optim
al s
ince
the
navi
gatio
n m
essa
ge ti
me
mar
k m
ust s
till b
e de
code
d.
20 m
, dep
endi
ng o
n th
e as
sist
ance
dat
a (F
igur
e 4)
.
BG
PS
A
pplic
able
. A
llow
s th
e us
e of
sig
nal
snap
shot
s in
mob
ile
term
inal
s w
ithou
t ne
twor
k co
nnec
tion.
Po
sitio
ns c
an b
e ca
lcul
ated
in
stan
tane
ousl
y, w
ithin
fr
actio
ns o
f a s
econ
d (P
etro
vski
et a
l., 2
008)
.
Doe
s no
t hav
e th
e ad
vant
age
of A
GPS
in
resp
ect o
f fre
quen
cy a
ssis
tanc
e to
con
stra
in
fron
tend
clo
ck d
rift
, whi
ch e
nabl
es lo
nger
in
tegr
atio
n pe
riod
s fo
r in
door
pos
ition
ing.
R
equi
res
inde
pend
ently
pre
dict
ed e
phem
eris
.
20 m
, but
can
be
impr
oved
su
bsta
ntia
lly in
PPP
mod
e.
SAG
PS
A
pplic
able
. R
educ
ed T
TFF
in s
igna
l-ch
alle
nged
env
iron
men
ts
is th
e ch
ief b
enef
it, b
ut
accu
racy
gai
ns m
ay a
lso
resu
lt (M
atto
s, 2
008)
.
Deg
rade
d ac
cura
cy a
fter
abo
ut th
ree
days
of
pred
icte
d ep
hem
eris
. Req
uire
s at
leas
t 30
0/40
0 M
Hz
CPU
.
Bet
ter
than
20
m fo
r ep
hem
eris
pre
dict
ion
age
less
th
an 2
4 ho
urs.
An assessment of location data requirements in logistics 195
Table 8 Taxonomy of modern location technologies with logistics potential (continued)
Tech
nolo
gy
Indo
or
appl
icab
ility
O
utdo
or
appl
icab
ility
Re
lativ
e m
erits
Re
lativ
e de
mer
its
Typi
cal i
ndoo
r and
out
door
ac
cura
cy (9
5%)
Dea
d re
ckon
ing
(see
not
e 1)
Odo
met
ry
App
licab
le b
ut,
beca
use
of it
s po
or a
ccur
acy,
it
is ra
rely
use
d in
door
s.
App
licab
le,
ofte
n in
tegr
ated
on
veh
icle
s by
auto
mak
ers.
Val
uabl
e as
whe
el
spee
d se
nsor
s (W
SS),
fo
r ant
ilock
bra
ke sy
stem
(A
BS)
, and
for i
nteg
ratio
n w
ith G
NSS
on
auto
mot
ives
.
Low
-cos
t pas
sive
odo
met
ers p
erfo
rm p
oorly
at
low
spee
ds (s
o ca
lled
dead
band
, nor
mal
ly
2 to
5 k
m p
er h
our)
. Act
ive
sens
ors,
base
d on
the
Hal
l effe
ct, g
ive
suffi
cien
tly st
rong
si
gnal
s at a
ll sp
eeds
but
are
exp
ensi
ve.
Opt
ical
sens
ors a
re v
ulne
rabl
e to
dirt
. M
eani
ngfu
l mod
ern
appl
icat
ions
requ
ire
inte
grat
ion
with
, e.g
., G
NSS
.
50 m
whe
n in
tegr
ated
with
G
NSS
. The
dom
inan
t err
or
sour
ce is
scal
e fa
ctor
err
ors d
ue
to u
ncer
tain
ty in
whe
el ra
dii.
Pede
stria
n de
ad
reck
onin
g A
pplic
able
but
m
ore
rese
arch
is
nee
ded
for
accu
raci
es o
f 1%
to
2%
.
App
licab
le b
ut
see
dem
erits
. U
sefu
l for
ped
estri
an
pers
onne
l mon
itorin
g, a
nd
for t
he m
ilita
ry a
nd fi
rst
resp
onde
rs.
Und
er c
lear
skie
s, G
NSS
pro
vide
s de
cim
etre
acc
urac
y. F
or p
edes
trian
na
viga
tion
in G
NSS
sign
al-c
halle
nged
en
viro
nmen
ts, p
edes
trian
mec
hani
satio
n m
ust b
e co
nsid
ered
. Err
ors d
epen
d on
pe
dest
rian
mov
emen
t cha
ract
eris
tics.
Enor
mou
s cha
lleng
es (i
nclu
ding
sens
or
mou
ntin
g an
d le
ver-
arm
dis
tanc
e) re
mai
n to
be
ove
rcom
e fo
r err
ors t
o be
less
than
1%
.
1% to
2%
of d
ista
nce
trave
lled.
Stan
dalo
ne IN
S In
door
ap
plic
atio
ns a
re
clea
rly li
mite
d be
caus
e of
the
unde
rlyin
g pr
inci
ples
.
App
licab
le
but a
dequ
ate
calib
ratio
n is
ne
eded
.
MEM
S-ba
sed
IMU
sy
stem
s hav
e w
ides
prea
d ap
plic
atio
ns, r
angi
ng fr
om
pede
stria
n na
viga
tion,
in
-car
nav
igat
ion,
bal
listic
m
issi
le g
uida
nce,
to sp
ace
appl
icat
ions
. The
y ha
ve
smal
l foo
tprin
t, lo
w
wei
ght,
and
are
pow
er
effic
ient
. Con
sum
er-g
rade
IM
Us a
re in
expe
nsiv
e bu
t ha
ve p
oor e
rror
char
acte
ristic
s.
Sens
or b
iase
s are
the
dom
inan
t sou
rce
of
erro
r. Th
ey h
ave
larg
e ra
ndom
wal
ks a
nd
vibr
atio
n re
ctifi
catio
n er
rors
. Sen
sors
are
al
so te
mpe
ratu
re se
nsiti
ve. A
slow
ly v
aryi
ng
bias
can
not b
e ca
libra
ted
prio
r to
field
de
ploy
men
t. 3
D g
yros
are
nee
ded
to
over
com
e m
ount
ing
prob
lem
s, bu
t thi
s in
crea
ses c
ost.
In g
ener
al, c
ost r
emai
ns a
co
ncer
n in
nav
igat
ion-
grad
e IN
S.
For n
avig
atio
n-gr
ade
IMU
, po
sitio
n er
ror i
s les
s tha
n
4 km
/h, g
yro
erro
r 0.0
15 d
eg/h
, ac
cele
rom
eter
err
or
0.1
mill
igal
.
196 A. Musa et al.
Table 8 Taxonomy of modern location technologies with logistics potential (continued)
Tech
nolo
gy
Indo
or
appl
icab
ility
O
utdo
or
appl
icab
ility
R
elat
ive
mer
its
Rel
ativ
e de
mer
its
Typi
cal i
ndoo
r an
d ou
tdoo
r ac
cura
cy (
95%
)
Fea
ture
mat
chin
g (F
EM
A)
(see
not
e 2)
Map
mat
chin
g (M
AM
A)
App
licab
le
but h
eigh
ts a
re
diff
icul
t with
out
3D m
odel
s of
bu
ildin
gs.
App
licab
le.
MA
MA
is n
ow a
key
fe
atur
e of
alm
ost a
ll la
nd
and
auto
nom
ous
vehi
cle
navi
gatio
n sy
stem
s.
FEM
A a
lso
has
seve
ral
appl
icat
ions
in a
vion
ics.
P
edes
tria
n na
viga
tion
in
urba
n ar
eas
is a
lso
a be
nefi
ciar
y. F
EM
A c
an
also
be
used
to d
eter
min
e a
prio
ri w
hat t
he e
ffec
ts o
f bu
ildin
gs w
ill b
e on
GN
SS
sign
als.
MA
MA
can
not c
ope
with
inst
ance
s in
w
hich
the
vehi
cle
is n
ot o
n th
e ro
ad, e
.g.,
whe
n pa
rkin
g or
on
new
and
unm
appe
d ro
ad. W
hen
the
vehi
cle
is o
n a
unid
irec
tion
al
mul
ti-la
ne r
oad,
MA
MA
can
not i
dent
ify
the
righ
t lan
e.
20 m
but
can
be
redu
ced
to
10 m
with
acc
urat
e m
ap
data
base
s.
Poin
t rec
ogni
tion
wit
hout
pre
inst
alle
d be
acon
s
App
licab
le.
App
licab
le.
Lea
ds to
uni
que
loca
tion
s by
pro
xim
ity
if th
e be
acon
s (r
efer
ence
poi
nts
or
obje
cts)
are
vis
ibly
dis
tinct
fr
om n
eigh
bour
hood
ob
ject
s an
d if
onb
oard
C
PU
ove
rhea
ds c
an b
e ac
com
mod
ated
.
Nee
ds p
edom
eter
s, a
ccel
erom
eter
s, g
yros
, m
agne
tom
eter
s fo
r m
easu
ring
trav
elle
d di
stan
ce a
nd h
eadi
ng. I
t is
not a
lway
s po
ssib
le to
fin
d un
ique
pos
ition
s, e
spec
iall
y if
thos
e po
sitio
ns a
re n
ot s
uita
bly
dist
inct
fr
om n
eigh
bour
ing
poin
ts. S
enso
rs m
ay b
e su
bjec
t to
low
vis
ibili
ty. I
ndoo
rs, f
urni
ture
an
d ot
her
item
s m
ay o
bstr
uct p
ositi
ons.
P
artic
le f
ilter
s m
ay b
e re
quir
ed to
arr
ive
at
opt
imal
sol
utio
ns a
nd th
ese
are
com
puta
tiona
lly in
tens
ive.
In p
rinc
iple
, the
re is
no
limit
to
accu
racy
if th
e ex
act l
ocat
ions
of
the
beac
ons
can
be f
ound
.
Loca
l rad
io n
avig
atio
n (s
ee n
ote
3)
Mob
ile te
leph
ony
netw
orks
. The
re
are
man
y te
chni
ques
in
this
cat
egor
y
(see
not
e 4)
App
licab
le.
App
licab
le.
Net
wor
k re
sour
ces
alre
ady
exis
t and
no
addi
tiona
l in
vest
men
t may
be
nece
ssar
y on
the
part
of
the
user
.
The
rel
ativ
ely
poor
acc
urac
y of
all
the
met
hods
in th
is c
ateg
ory
lim
its th
e us
e
of th
ese
tech
niqu
es in
inte
grat
ed n
avig
atio
n sy
stem
s. F
utur
e G
NSS
met
hods
may
m
ake
cellu
lar
met
hods
less
attr
activ
e. I
n la
rge-
scal
e em
erge
ncie
s, c
ellu
lar
reso
urce
s m
ay b
e co
mpr
omis
ed b
y di
sast
er o
r ov
erw
helm
ed b
y de
man
d.
50 to
200
m, d
epen
ding
on
the
met
hod
used
and
mea
sure
men
t co
nditi
ons.
Shor
t-ra
nge
wir
eles
s ne
twor
ks. S
ee n
ote
5 fo
r th
e m
any
sign
al
type
s av
aila
ble
for
this
met
hod
App
licab
le.
App
licab
le.
The
se s
omet
imes
use
ex
istin
g in
fras
truc
ture
(e
spec
iall
y in
the
case
of
WiF
i) a
nd s
o ar
e re
lativ
ely
chea
p w
here
su
ch in
fras
truc
ture
exi
sts.
In
man
y ap
plic
atio
ns,
how
ever
, the
infr
astr
uctu
re
has
to b
e sp
ecia
lly
esta
blis
hed
(Čap
kun
and
Hub
aux,
200
6; B
ensk
y,
2008
; Ret
sche
r, 2
006,
20
07; R
etsc
her
et a
l.,
2007
).
Pre
inst
alle
d in
fras
truc
ture
is n
eces
sary
, or
(in
the
case
of
ad h
oc n
etw
orks
) m
ust b
e es
tabl
ishe
d du
ring
the
surv
ey. M
ulti
path
and
lim
ited
pene
trat
ion
capa
bilit
y of
the
sign
als
can
be a
hin
dran
ce in
door
s. F
or o
utdo
or
appl
icat
ions
, a la
rge
num
ber
of n
etw
ork
node
s is
oft
en n
eces
sary
.
Shor
t-ra
nge
beac
ons
and
UW
B
can
give
sub
-met
re a
ccur
acie
s.
By
usin
g fi
nger
prin
ting
and
RS
S, W
iFi c
an y
ield
1 to
5 m
ac
cura
cy (
Win
and
Sch
oltz
, 19
98).
An assessment of location data requirements in logistics 197
Table 8 Taxonomy of modern location technologies with logistics potential (continued)
Tech
nolo
gy
Indo
or
appl
icab
ility
O
utdo
or
appl
icab
ility
Re
lativ
e m
erits
Re
lativ
e de
mer
its
Typi
cal i
ndoo
r an
d ou
tdoo
r ac
cura
cy (9
5%)
Loca
l rad
io n
avig
atio
n (s
ee n
ote
3)
TV a
nd F
M s
igna
ls
(see
not
e 6)
A
pplic
able
. A
pplic
able
. Th
ese
use
exis
ting
infr
astru
ctur
e at
rela
tivel
y ch
eap
cost
. Car
rier-
phas
e po
sitio
ning
is p
ossi
ble
w
ith a
ny ra
dio
sign
al.
TV s
igna
ls h
ave
larg
e po
wer
, low
er fr
eque
ncy
(54
to 8
00 M
Hz)
, hig
h fr
eque
ncy
dive
rsity
, and
lo
wer
gra
zing
ang
le
(hen
ce le
ss a
ttenu
atio
n).
Posi
tioni
ng a
ccur
acy
has
not i
mpr
oved
, du
e to
(am
ong
othe
r rea
sons
) mul
tipat
hing
an
d di
ffic
ultie
s in
err
or m
odel
ling.
In
man
y ci
ties,
par
ticul
arly
in E
urop
e, th
e tra
nsm
itter
s fo
r diff
eren
t net
wor
ks a
re o
ften
all l
ocat
ed a
t the
sam
e si
te, t
here
by li
miti
ng
geog
raph
ic s
prea
d an
d si
gnal
geo
met
ry.
5 to
20
m, d
epen
ding
on
how
se
rious
mul
tipat
h ef
fect
s ar
e.
Pseu
dolit
es.
Thes
e ar
e gr
ound
tra
nsce
iver
s th
at
trans
mit
and
rece
ive
GPS
-like
sig
nals
App
licab
le.
App
licab
le.
GPS
rece
iver
s ca
n re
ceiv
e an
d de
code
pse
udol
ite
sign
als,
just
like
pse
udol
ite
rece
iver
s ca
n re
ceiv
e an
d de
code
GPS
sig
nals
. In
crea
ses
avai
labi
lity,
ac
cura
cy a
nd in
tegr
ity
both
indo
ors
and
outd
oors
. U
sed
in n
iche
mar
kets
, e.
g., p
reci
sion
app
roac
h an
d la
ndin
g of
airc
rafts
.
Pseu
dolit
e’s
use
of a
C/A
cod
e-lik
e si
gnal
m
eans
that
the
rece
ived
pse
udol
ite s
igna
l ca
n be
mor
e th
an 2
0 dB
stro
nger
than
the
GPS
C/A
-cod
e, th
us le
adin
g to
inte
rfer
ence
. Pu
lsin
g th
e ps
eudo
lite
sign
al c
an h
elp.
H
ardw
are
cost
can
be
high
(tho
usan
ds
of d
olla
rs).
Pseu
dolit
es a
re n
ot ti
me
sync
hron
ised
with
GN
SS o
r with
in th
e sy
stem
itse
lf, m
akin
g di
ffer
entia
l ope
ratio
ns
nece
ssar
y. A
ttem
pts
to s
ynch
roni
se
pseu
dolit
es h
ave
led
to la
rger
pos
ition
ing
erro
rs.
In d
iffer
entia
l mod
e, c
entim
etre
le
vel a
ccur
acie
s ar
e po
ssib
le.
Loca
talit
es
App
licab
le.
App
licab
le.
This
is a
ctua
lly a
noth
er
type
of p
seud
olite
, bu
t sol
ves
mos
t of t
he
diff
icul
ties
of c
onve
ntio
nal
pseu
dolit
e. C
an tr
ack
both
lo
cata
lite
and
GPS
sig
nals
, th
us a
llow
ing
seam
less
fu
nctio
nalit
y. H
ighl
y
time-
sync
hron
ised
w
ithou
t ato
mic
clo
cks,
th
us e
nabl
ing
prec
ise
sing
le-p
oint
pos
ition
ing.
Sc
alab
le: c
an b
e us
ed fo
r sm
all a
nd w
ide
area
ap
plic
atio
ns. C
ost
effe
ctiv
e. H
igh
relia
bilit
y.
Can
be
used
to b
uild
ad
hoc
net
wor
ks fo
r em
erge
ncy
oper
atio
ns.
The
tech
nolo
gy is
pat
ente
d by
Loc
ata
Cor
pora
tion
of C
anbe
rra
(Bar
nes
et a
l.,
2003
; Mar
tin e
t al.,
200
7) a
nd d
evel
opm
ent
effo
rts s
eem
to h
ave
wan
ed in
rece
nt ti
mes
. H
ow a
nd th
e ex
tent
to w
hich
loca
talit
es
sign
als
avoi
d or
redu
ce in
terf
eren
ce w
ith
GN
SS h
as n
ot b
een
inve
stig
ated
out
side
of
Loc
ata
Cor
pora
tion.
As
in c
onve
ntio
nal
pseu
dolit
es, i
ssue
s re
mai
n, in
clud
ing
tropo
sphe
ric e
rror
mod
ellin
g, in
door
sig
nal
pene
tratio
n de
lays
, sel
ectio
n of
opt
imal
fr
eque
ncie
s an
d be
st ra
ngin
g si
gnal
st
ruct
ures
, and
inte
grat
ion
with
INS
an
d im
agin
g se
nsor
s.
Sub-
cent
imet
re a
ccur
acy
is
poss
ible
in b
oth
diff
eren
tial a
nd
non-
diff
eren
tial m
odes
, ind
oors
an
d ou
tdoo
rs.
198 A. Musa et al.
Table 8 Taxonomy of modern location technologies with logistics potential (continued)
Tech
nolo
gy
Indo
or
appl
icab
ility
O
utdo
or
appl
icab
ility
R
elat
ive
mer
its
Rel
ativ
e de
mer
its
Typi
cal i
ndoo
r an
d ou
tdoo
r ac
cura
cy (9
5%)
Loca
l rad
io n
avig
atio
n (s
ee n
ote
3)
Indo
or m
essa
ging
sy
stem
(IM
ES)
from
Ja
pan
Aer
ospa
ce
Exp
lora
tion
Age
ncy
App
licab
le.
App
licab
le
whe
re th
ere
ar
e IM
ES
tran
smitt
ers.
L
imite
d ra
nge.
Low
pow
er c
onsu
mpt
ion,
lo
w tr
ansm
itter
cos
t, an
d av
aila
bilit
y bo
th in
door
s an
d ou
tdoo
rs (
Man
andh
ar
et a
l., 2
008)
.
Pote
ntia
l for
inte
rfer
ence
with
GN
SS
sign
als
(see
the
desc
ript
ion
of I
ME
S in
the
App
endi
x), a
lthou
gh n
ew G
PS s
igna
ls s
uch
as L
2C c
an d
imin
ish
the
risk
. The
cos
t of t
he
need
ed p
rein
stal
led
infr
astr
uctu
re c
an b
e hi
gh.
10 m
POSC
OM
M
(pos
ition
ing
and
com
mun
icat
ions
sy
stem
fro
m
NA
VSY
S C
orpo
ratio
n,
Col
orad
o Sp
ring
s,
CO
.)
App
licab
le.
App
licab
le.
POSC
OM
M’s
sof
twar
e-de
fine
d ra
dios
(SD
Rs)
are
co
nfig
ured
to o
pera
te a
s bo
th G
PS r
ecei
ver
and
a 90
0 M
Hz
tran
scei
ver.
T
he p
ositi
onin
g
serv
ice
leve
rage
s bo
th
GPS
-der
ived
pse
udor
ange
s an
d ca
rrie
r-ph
ase
obse
rvat
ions
toge
ther
w
ith th
e co
mm
unic
atio
ns
chan
nel’
s T
OA
m
easu
rem
ents
. The
sys
tem
is
bas
ed o
n lo
w-c
ost,
of
f-th
e-sh
elf h
ardw
are
and
soft
war
e. F
or m
axim
um
flex
ibili
ty, t
he m
ultip
le
mas
ter
units
pro
vidi
ng
TO
A a
ssis
tanc
e sh
are
the
avai
labl
e sp
ectr
um u
sing
ei
ther
CM
DA
, TD
MA
, or
FDM
A. T
he a
ssoc
iate
d vi
deo
cam
era
prov
ides
re
gist
ered
imag
ery
of th
e sc
ene
for
situ
atio
nal
awar
enes
s an
d m
appi
ng.
The
aut
hors
cou
ld n
ot e
stab
lish
the
unit
cost
of
the
prod
uct b
ut b
elie
ve th
at it
is h
igh.
The
co
mm
unic
atio
ns c
hann
el m
ay in
terf
ere
with
th
e G
PS r
ecei
ver
chan
nel,
and
it m
ay b
e im
poss
ible
to c
omm
unic
ate
whi
le tr
acki
ng.
Inte
grat
ion
of a
ME
MS
IMU
ass
ists
in
filte
ring
and
trac
king
of T
OA
mea
sure
men
ts
in e
nvir
onm
ents
with
deg
rade
d G
PS s
igna
ls.
5 m
An assessment of location data requirements in logistics 199
Table 8 Taxonomy of modern location technologies with logistics potential (continued)
Tech
nolo
gy
Indo
or
appl
icab
ility
O
utdo
or
appl
icab
ility
Re
lativ
e m
erits
Re
lativ
e de
mer
its
Typi
cal i
ndoo
r and
out
door
ac
cura
cy (9
5%)
Inte
grat
ed n
avig
atio
n G
NSS
+IN
S A
pplic
able
but
in
door
s (w
ithou
t ZU
PT fr
om G
NSS
or
oth
er s
ourc
es)
the
erro
r of I
NS
is
like
ly to
gro
w
quic
kly.
App
licab
le.
This
is s
o fa
r the
m
ost i
mpo
rtant
sen
sor
fusi
on s
trate
gy. G
NSS
m
easu
rem
ents
pre
vent
the
time-
depe
nden
t drif
t of
INS
solu
tions
, whi
le IN
S m
easu
rem
ents
sm
ooth
the
GN
SS s
olut
ion
and
brid
ges
sign
al g
aps.
GN
SS+I
NS
inte
grat
ion
also
mak
es
INS
prac
tical
with
low
er
cost
tact
ical
-gra
de IN
S se
nsor
s (A
bdel
-Ham
id
et a
l., 2
006)
.
The
navi
gatio
n m
odul
e be
com
es m
ore
expe
nsiv
e th
an s
tand
alon
e sy
stem
s. T
he
estim
atio
n pr
oble
ms
beco
me
nonl
inea
r an
d va
rious
var
ietie
s of
ext
ende
d K
alm
an
filte
r and
arti
ficia
l neu
ral n
etw
orks
(G
rejn
er-B
rzez
insk
a et
al.,
200
7; T
hien
elt
et a
l., 2
007)
hav
e to
be
used
for m
any
prac
tical
pro
blem
s, re
quiri
ng p
rem
ium
on
boar
d C
PU a
nd p
rior c
alib
ratio
n.
Nor
mal
ly p
erfo
rms
bette
r tha
n st
anda
lone
sys
tem
s. A
ccur
acy
depe
nds o
n th
e qu
ality
of
GN
SS re
ceiv
er a
nd IN
S se
nsor
, th
e ty
pe o
f int
egra
tion
(loos
e,
tight
, ultr
a-tig
ht a
nd d
eep)
, and
es
timat
ion
met
hods
use
d.
GN
SS+I
NS+
CSA
C
App
licab
le.
App
licab
le.
Prec
ise
time
rem
oves
one
of
the
dom
inan
t sou
rces
of
err
ors
in p
ositi
onin
g by
G
NSS
.
Still
in it
s inf
ancy
and
uni
t cos
t rem
ains
hi
gh.
1 m
or b
ette
r
GN
SS+I
NS+
La
ser s
cann
er
App
licab
le
but c
ost i
s an
im
porta
nt fa
ctor
.
App
licab
le.
The
num
ber o
f au
tom
otiv
es c
arry
ing
lase
r ra
ngef
inde
rs is
incr
easi
ng
and
thes
e ca
n be
exp
loite
d fo
r int
egra
ted
navi
gatio
n.
Lase
rs o
ffer
the
adva
ntag
e of
inst
anta
neou
s 3D
ca
ptur
e w
ithou
t mot
ion
arte
fact
s in
here
nt to
dy
nam
ic s
cann
ing
sens
ors.
Th
e na
scen
t tec
hnol
ogy
of
flash
LiD
AR
(3D
cam
eras
) of
fers
the
poss
ibili
ty o
f in
crea
sing
the
func
tiona
lity
of la
sers
, e.g
., fo
r mob
ile
map
ping
.
The
use
of la
ser s
cann
ers
requ
ires
accu
rate
G
IS d
atab
ase
of th
e ge
ogra
phy.
The
mor
e th
e nu
mbe
r of s
enso
rs in
an
inte
grat
ed
navi
gatio
n so
lutio
n, th
e m
ore
the
com
plex
ity
of th
e es
timat
ion
prob
lem
and
hen
ce th
e gr
eate
r the
nee
d fo
r pre
miu
m o
nboa
rd
proc
esso
rs. T
he in
here
nt h
ardw
are
cost
m
akes
this
tech
niqu
e cu
rren
tly v
iabl
e fo
r on
ly a
dvan
ced
avio
nics
and
tran
spor
tatio
n.
Acc
urac
y de
pend
s on
the
qual
ity o
f the
inte
grat
ed s
enso
rs
and
estim
atio
n al
gorit
hms
used
. O
pera
tiona
l-gra
de la
sers
off
er a
ra
ngin
g ac
cura
cy o
f 2 c
m a
nd
max
imum
rang
ing
dist
ance
of
abou
t 200
m.
200 A. Musa et al.
Table 8 Taxonomy of modern location technologies with logistics potential (continued)
Tech
nolo
gy
Indo
or
appl
icab
ility
O
utdo
or
appl
icab
ility
Re
lativ
e m
erits
Re
lativ
e de
mer
its
Typi
cal i
ndoo
r and
out
door
ac
cura
cy (9
5%)
Inte
grat
ed n
avig
atio
n
Coo
pera
tive
met
hods
(see
not
e 7)
A
pplic
able
. A
pplic
able
. C
an im
prov
e th
e po
sitio
ning
per
form
ance
fo
r an
entir
e gr
oup
of u
nits
or
per
sonn
el. D
iffer
ent u
nit
mem
bers
may
be
kitte
d w
ith e
quip
men
t of
diffe
ring
soph
istic
atio
n.
Suita
ble
for a
nti-j
amm
ing
stra
tegi
es.
Uni
t cos
t can
be
exor
bita
nt b
ecau
se o
f the
ne
ed fo
r coo
pera
tion
and
for m
ulti-
sens
or
inte
grat
ion.
Dep
ends
on
the
unde
rlyin
g te
chno
logi
es.
Not
es: 1
D
ead
reck
onin
g (D
R) i
s the
pro
cess
of e
stim
atin
g on
e’s c
urre
nt p
ositi
on b
ased
on
a pr
evio
usly
det
erm
ined
pos
ition
, and
adv
anci
ng th
at p
ositi
on b
ased
on,
e.g
.,
spee
d, e
laps
ed ti
me,
and
cou
rse.
2
Feat
ure
mat
chin
g sc
hem
es in
clud
e te
rrai
n-re
fere
nced
nav
igat
ion
(TR
N),
imag
e m
atch
ing
(esp
ecia
lly w
ith L
iDA
R-g
ener
ated
terr
ain
data
base
), m
ap m
atch
ing,
an
d st
ella
r nav
igat
ion.
In g
ener
al, t
hese
tech
niqu
es d
eter
min
e th
e us
er’s
pos
ition
by
mea
surin
g fe
atur
es o
f the
env
ironm
ent (
e.g.
, ter
rain
hei
ghts
or r
oads
) an
d co
mpa
ring
them
with
a d
atab
ase,
sim
ilar t
o th
e m
anne
r tha
t a p
edes
trian
wou
ld c
ompa
re la
ndm
arks
with
a m
ap o
r a m
enta
l pic
ture
of t
he te
rrai
n. F
eatu
re
mat
chin
g sy
stem
s nee
d in
itial
isat
ion
with
an
appr
oxim
ate
posi
tion
so a
s to
dete
rmin
e th
e re
gion
of t
he d
atab
ase
whe
re to
com
men
ce th
e se
arch
. Lim
iting
the
data
base
sear
ch a
rea
natu
rally
redu
ces t
he c
ompu
tatio
nal o
verh
ead
and
the
num
ber o
f ins
tanc
es in
whi
ch th
ere
are
mor
e th
an o
ne m
atch
bet
wee
n th
e m
easu
red
feat
ures
and
thos
e in
the
data
base
. To
be a
ble
to d
eter
min
e th
e re
lativ
e po
sitio
ns o
f the
mea
sure
d fe
atur
es, m
ost i
mpl
emen
tatio
ns a
lso
requ
ire a
vel
ocity
so
lutio
n, u
sual
ly fr
om a
n IN
S or
oth
er d
ead-
reck
onin
g se
nsor
. Fea
ture
mat
chin
g is
thus
not
an
inde
pend
ent n
avig
atio
n so
lutio
n or
tech
niqu
e; it
is in
deed
one
of
the
inte
grat
ed n
avig
atio
n sy
stem
s. M
oreo
ver,
all f
eatu
re m
atch
ing
tech
niqu
es a
re li
able
to o
ccas
iona
l err
oneo
us fi
x, d
ue e
ither
to th
e ag
e of
the
data
base
or,
whe
re th
ere
are
mul
tiple
mat
ches
, sel
ectin
g th
e w
rong
mat
ch. T
he in
here
nt in
tegr
ated
nat
ure
of fe
atur
e m
atch
ing
does
hel
p in
such
diff
icul
t sce
nario
s. 3
New
dev
elop
men
ts in
indo
or lo
catio
n te
chni
ques
bas
ed o
n te
rres
trial
radi
o si
gnal
s hav
e re
lativ
ely
rece
ntly
em
erge
d. S
yste
ms a
vaila
ble
on th
e m
arke
t use
si
gnal
s suc
h as
infra
red,
ultr
ason
ic a
nd ra
dio.
Mos
t of t
hese
syst
ems,
how
ever
, req
uire
exp
ensi
ve in
stal
latio
ns o
f a la
rge
num
ber o
f rec
eive
rs o
r tra
nsm
itter
s in
the
indo
or e
nviro
nmen
t. To
redu
ce in
stal
latio
n co
sts,
an a
ppro
ach
may
be
chos
en w
hich
mak
es u
se o
f alre
ady
avai
labl
e in
fras
truct
ure,
i.e.
, the
use
of W
irele
ss
LAN
(WLA
N o
r WiF
i).
4 M
obile
tele
phon
y m
etho
ds a
re d
ivid
ed in
to tw
o m
ain
subc
ateg
orie
s: te
rmin
al-b
ased
and
net
wor
k-ba
sed.
With
in th
ese
subc
ateg
orie
s are
AG
PS, S
IM-to
olki
t, ce
ll-ID
, OTD
A (o
bser
ved
time
diff
eren
ce o
f arr
ival
), U
L-TD
A (u
plin
k tim
e di
ffere
nce
of a
rriv
al),
E-O
TDA
(enh
ance
d O
TDA
), A
OA
(ang
le o
f arr
ival
), C
GI+
TA (c
ell g
loba
l ide
ntity
tim
ing
adva
nce)
, E-C
GI+
TA (e
nhan
ced
CG
I+TA
), m
atrix
, etc
. 5
Shor
t-ran
ge w
irele
ss n
etw
ork
tech
niqu
es in
clud
e th
e us
e of
WLA
N (I
EEE8
02.1
1), R
FID
, Zig
Bee
(IEE
E802
.15.
4), B
luet
ooth
, ad
hoc
netw
orks
, UW
B
(IEE
E802
.15.
3), u
ltras
ound
, GN
SS re
peat
ers (
Esm
ond
et a
l., 2
007;
Ben
sky,
200
8).
6 TV
, AM
and
FM
sign
als a
re c
alle
d ‘s
igna
ls o
f opp
ortu
nity
’ bec
ause
they
nor
mal
ly e
xist
for p
urpo
ses o
ther
than
loca
tion
dete
rmin
atio
n.
7 C
oope
rativ
e po
sitio
ning
met
hods
are
man
y, d
epen
ding
mos
tly o
n th
e op
erat
ing
envi
ronm
ent.
One
typi
cal e
xam
ple
is a
gro
up o
f firs
t-res
pond
ers o
n th
e pe
riphe
ry o
f an
inci
denc
e zo
ne (e
.g.,
a bu
rnin
g bu
ildin
g) p
rovi
ding
loca
tion
refe
renc
e da
ta (t
hrou
gh w
eara
ble
pseu
dolit
es o
r ad
hoc
netw
ork
sign
al so
urce
s)
to th
eir c
olle
ague
s ins
ide
the
dang
er z
one.
Firs
t-res
pond
ers i
nsid
e th
e da
nger
zon
e m
ay a
lso
exch
ange
loca
tion
refe
renc
e da
ta if
som
e of
them
hav
e ac
cess
to
unco
mpr
omis
ed p
rein
stal
led
asse
ts fo
r loc
atio
n de
term
inat
ion.
An assessment of location data requirements in logistics 201
Loran too has been omitted from the table. As mentioned in Section 3.3, Loran is an internationally standardised positioning, navigation, and timing (PNT) service for use by many modes of transport and other applications. eLoran has been proved to have the capacity to provide the performance in accuracy, availability, integrity, and continuity needed for applications like aviation non-precision instrument approaches, maritime harbour entrance and approach, land-mobile vehicle navigation, and location-based services. It may also be deployed as a source of precise time and frequency for services like telecommunications. It is an independent and dissimilar complement to GNSS and allows GNSS users to maintain the safety, security, and economic benefits of GNSS even when their satellite services are disrupted by, e.g., deliberate signal jamming. Despite all these advantages of Loran, however, it has been phased out by the US Coast Guard and its global future outlook is arguably uncertain.
Figure 4 Assisted-GPS system architecture
Location server
Referencestations
Cellular network
GNSS satellites
A-GPS receiver
Technologies such as automatic identification systems (AIS) and long range identification and tracking system (LRIT) are still in currency but they have been omitted from Table 8 so as to keep the size of the table to proportion. These are used only in the maritime industry. Also omitted from Table 8 are point-based navigation systems, which provide horizontal positions using measurements from only one station, although multiple stations are applicable. These methods include non-directional beacon broadcasts (NDBs), VOR (VHF omnidirectional radio-range), and DME (distance measurement equipment). Details of these methods can be found in, e.g., Enge et al. (1995), and Uttam et al. (1997). Also left out of Table 8 is the use of sonar for underwater location. Radio navigation signals do not propagate underwater. Instead, submarines, remotely operated vehicles (ROVs), and autonomous underwater vehicles (AUVs) rely on sonar for underwater location (Butler and Verrall, 2001).
202 A. Musa et al.
A key to the success of logistics application of location is accurate georeferencing of the location obtained from a navigation system. Georeferencing involves relating the location or position to a map database, e.g., to identify the road on which a vehicle is travelling or a delivery van or a salesman is located. Systems that combine navigation and map database would be effective only if the following factors are fulfilled: affordability to the user; accuracy of approximately 20 m outdoors at 95% confidence level; accuracy of about 10 cm indoors at 95% confidence level; automatic initialisation without user assistance; effective display of location or position and location-based application; and a reliable map database.
5 Conclusions and future research
Location, navigation and precise timing have gradually become commodities, not just for logistics applications but for several other purposes. Location information, especially as derived from GPS, is now at the heart of most transportation capabilities, logistics and distribution industries, just-in-time manufacturing, emergency service operations, extractive industries, road construction, agriculture, etc. Even more notable is the fact that GPS, a system conceived and implemented entirely for positioning, provides the high-precision timing that forms the system backbone of many telephone networks, power grids, the internet, high-speed share trading, banking transactions, and many other key sectors of the economy and social infrastructure. Location information could be used, for instance, to support companies operating toll roads to collect fees without drivers having to stop, and charging them only for the exact distance they travelled. Other logistics and engineering projects that require a high-level of accuracy could also achieve centimetre-level measuring accuracy. Supplemental information could also be disseminated through new communication channels, in addition to the satellite broadcasts, such as via radio, SMS, GPRS, EDGE, WIDI, WiMAX and other emerging standards (Andrews et al., 2007).
This paper has presented requirements for location information for several logistics applications across industries. The information provided herein is handy and fulfils a need of the logistics industry and allied sectors, including the semiconductor industry. For specific projects and applications, however, it would be necessary to conduct detailed preliminary studies and assessments to determine the exact needs as well as technology and operational limitations. Table 8 provides a classification of the plethora of location technologies together with their capabilities as well as their relative advantages and disadvantages.
The special Delphi method introduced and operationalised by this paper is more laborious than the conventional Delphi approach but, since it uses a larger panel than the traditional method, its results are more robust by being more representative of reality. It may be used in other empirical investigations that rely on the Delphi method and where a larger panel or robustness of the results is desired or necessary.
5.1 Future research
It is recommended that the empirical methodology adopted in this paper be complemented with simulation studies, the complexities introduced by the myriad of application scenarios and available location technologies notwithstanding.
An assessment of location data requirements in logistics 203
Experimentation with ‘probe missions’ is also needed to buttress and confirm the validity and utility of the estimated requirements.
There are several challenges that remain to be addressed in respect of location technologies for indoor and outdoor logistics, all of which offer avenues for further research. They include: signal multipathing and attenuation effects especially indoors; signal interference, particularly regarding the shared frequencies between GPS, pseudolites and IMES; height estimation, especially in indoor environments; estimating the heading when using MEMS-based sensors; the development of advanced, reliable and efficient signal processing and sensor fusion algorithms; empowering location and navigation devices to use contexts and learn and estimate places (and correctly suggesting place names), in addition to geo-coordinates, in order to speed up positioning even in signal-challenged environments and also to reduce power consumption; and generating so-called ‘2.5-dimensions’, in which altitude is represented with a symbolic name such as ‘parking level A’ or ‘3rd floor’. Height representation by 2.5-dimensions are more meaningful to the user than coordinate-based altitude like ‘3.6 metres above vertical reference’. Also, some of the challenges associated with specific positioning technologies, as highlighted in Table 8 against each method or technology, provide opportunities and challenges for further research.
The integration of multiple location sensors and systems often leads to non-linear and non-Gaussian estimation problems. For such problems the conventional extended Kalman filter may be inadequate and other filter types, such as unscented Kalman filter and/or the particle filter, may be more suitable. Unfortunately, these advanced filters generally have higher computational overheads, which in turn imply higher onboard power budgets and superior grade processors. Sensor fusion can be implemented as a centralised or decentralised filter, the major trade-off between them being optimum performance versus computational complexity.
References Abdel-Hamid, W., Abdelazim, T., El-Sheimy, N. and Lachapelle, G. (2006) ‘Improvement of
MEMS-IMU and GPS performance using fuzzy modelling’, GPS Solutions, Vol. 10, No. 1, pp.1–11.
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Appendix
ADAS Advanced driver assistance system. ADS-B Automatic dependent surveillance broadcast. AGPS Assisted GPS. A cold start for a GPS receiver typically takes between 60 seconds
to 12 minutes. A warm start takes about 30 seconds in ideal conditions, and a hot start takes 6 seconds or more. AGPS can reduce the TTFF (time to first fix of position) by supplying current ephemeris and accurate time over a communications connection. In some situations, TTFF can be reduced to just one or two seconds, the time needed to acquire only one subframe of navigation data, to retrieve the time mark. However, the receiver does need a network connection to the source of the assistance data, or direct USB access to a PC. The receiver thus cannot operate autonomously. Moreover, it is often impossible to access time information through a communications network accurate enough to obviate reading at least one subframe of the navigation message. Then there is the issue of privacy: users may just want to navigate without identifying themselves and revealing their location to the network assistance provider. There is also the issue of interference between voice calls and GPS functions on mobile phones, requiring the user to wait until all the data from the navigation message is acquired before placing calls. Assisted GPS protocols include those using GSM, GPRS, or CDMA networks. In GSM the assistance data is specified in the Radio Resource Location Services Protocol (RRLP), and in UMTS it is given in Radio Resource Control (RRC). There are also user-plane solutions, e.g., Open Mobile Alliance’s Secure User Plane Location (SUPL), which effectively transmits the same information over packet-switched networks that are specified for circuit-switched networks in RRLP and RRC.
AGV Automated guided vehicle. AIS Automatic identification systems. ALV Automated lift vehicle. APV Approach procedures with vertical guidance (with APV-I and APV-II. See
Section 3.2). ASSA Airport surface situational awareness. AUV Autonomous underwater vehicles. BGPS The ‘B’ in the acronym ‘BGPS’ simply means ‘this comes after A’. BGPS
positively answers the question of whether it is possible to realise AGPS without requiring a network and without reading a navigation message from a satellite signal (Petrovski et al., 2008).
CAPS Chinese area positioning system. Less well known than Beidou and Compass, even in the navigation community, is China’s other regional satellite navigation system called CAPS. This operates on C-band frequencies, instead of the L band in which most GNS systems operate. CAPS also differs from all other GNS systems in the sense that the navigation messages are generated on the ground and uploaded to the communications satellites, with the satellites serving only as transponders.
CAT Category (with CAT-I, CAT-II, etc. See Section 3.2). CR Containment radius of position fix.
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CSAC Chip-scale atomic clocks. This an emerging technology that is still in its infancy (Kitching et al., 2005; Knappe et al., 2004, 2005). Applications have so far been limited to the military. Of the most significant sources of errors and computational overheads in positioning with GNSS is the fact that the receiver clocks are relatively cheap but imprecise. CSAC technology hopes to make affordable atomic clocks of chip size, for incorporation in high-end GNSS receivers. The ‘positioning nugget’ technology represents the convergence of a CSAC with a deeply integrated MEMS inertial measurement unit, a GPS M-code software-defined receiver (SDR), and particle-filtering accelerator (Ristic et al., 2004; Rollo, 2007). If a GNSS receiver has a tiny atomic clock that is synchronised with the atomic clocks of the GNS system, then one of the major equations the receiver CPU has to solve disappears and the following become realisable: significantly reduced TTFF; reduced position error for time and altitude with fewer satellites in view; increased anti-jamming and anti-spoofing capability; decreased GNSS reacquisition time when the signal is temporarily lost; the GNSS transceiver can become a master member of a cooperative positioning network, i.e., it can broadcast its location corrections and precise time to other receivers in the network.
DARPA Defence Advanced Research Projects Agency of the USA. DASH7 Developers’ Alliance for Standards Harmonization of ISO 18000-7. Unlike most
active RFID technologies, DASH7 supports tag-to-tag communication. dB Decibel. DGPS Differential GPS. DME Distance measurement equipment. DSRC Dedicated short-range communication. DSS Digital signal standard. EDGE Enhanced data rates for GSM evolution. EEBL Emergency electronic brake light. EGNOS European geostationary navigation overlay service (see SBAS). EPU Estimated position uncertainty. Exif Exchangeable image file format (often incorrectly written as EXIF) is a standard
that specifies the formats for images, sound, and ancillary tags used by digital cameras (including smartphones), scanners and other systems handling image and sound files recorded by digital cameras.
FAA US Federal Aviation Authority. FARO Final approach and runway occupancy awareness. FCC US Federal Communications Commission. FCW Forward collision warning. GAGAN India’s GNSS-aided geosynchronous augmented navigation system (see SBAS). GAST-D GBAS Approach Service Type D. GBAS Ground-based augmentation systems. GDP Gross domestic product. GLONASS Russia’s global navigation satellite system. GNSS This is a generic term for global satellite navigation system. GPRS General packet radio service. HS-GPS High-sensitivity GNSS. ICAO International Civil Aviation Organization.
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IMES Japan’s indoor messaging service/system. This is composed of transmitters, GPS receivers with modified firmware embedded in mobile terminals and servers. It aims to provide seamless positioning everywhere indoors. The system has been developed by Japan’s Aerospace Exploration Agency (JAXA), GNSS Technologies, and Lighthouse Technology and Consulting. Hitachi Ltd is also working on a similar system. It relies on satellite signals outdoors but uses signals from IMES transmitters indoors. The IMES signal structure is similar to that of GPS, except for the content of the navigation message. Thus, the same receiver can be used for both GPS and IMES. An IMES transmitter sends an RF signal similar to that of GPS and QZSS, suggesting its 3D location, the position of the center of its cell coverage area, or linking the receiver to a database that provides the location and other characteristics of the transmitter. Instead of the ephemeris data, clock corrections, ionospheric parameters, etc., contained in the GPS navigation message, the IMES message periodically broadcasts position and additional information in a similar format. IMES uses the same L1 centre frequency as GPS and QZSS, and the same BPSK modulation. IMES’s dedicated spread spectrum codes are from the same family of Gold Codes as GPS and QZSS (numbers 173 to 182 from the C/A code assignment table). The power of each IMES transmitter is low (0.1 to 0.4 nanowatts) that it can only be acquired within about 10 m of the transmitter. Any GPS receiver that can decode PRNs 173 through 182 can receive and decode IMES signals.
IMU Inertial measuring unit. The sensor unit containing inertial sensors (accelerometers and gyros).
INS Inertial navigation system. IP Internet Protocol. IP address location data can include information such as
country, region, city, postal/zip code, latitude, longitude and time zone. Deeper data sets can determine other parameters such as domain name, connection speed, ISP, language, proxies, company name, US DMA/MSA, NAICS codes, and home/business.
IPTC The International Press Telecommunications Council, based in London, UK, is a consortium of the world’s major news agencies and news industry vendors. It develops and maintains technical standards for improved news exchange that are used by virtually every major news organisation in the world.
IRNSS Indian regional navigation satellite system (see SBAS). IVS In-vehicle signing. LAAS GPS local area augmentation system. LBS Location-based services. LCA Lane change advisor. LiDAR Light detection and ranging. LNAV Lateral navigation. LPV Localizer performance with vertical guidance. LRIT Long range identification and tracking system. MASPS Minimum aviation system performance standards. MEMS Micro-electro-mechanical system. A manufacturing process that, e.g., enables the
design and fabrication of miniaturised, lightweight, power efficient, and potentially inexpensive sensors, including accelerometers, gyros and magnetometers (Nguyen, 2007).
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MS Mobile station. MSAS Japan’s MTSAT-satellite-based augmentation system (see SBAS). MTSAT Japan’s multifunction transport satellite. NAC-P Navigation accuracy category for position. NDB Non-directional beacon broadcast. NIC Navigation integrity category. NRA Non-radar airspace. NRTK Network real-time kinematic positioning. OEM Original equipment manufacturer. PNT Positioning, navigation, and timing. PPP Precise point positioning by GNSS signals. QZSS Japan’s quasi-zenith navigation satellite system (see SBAS). The inclined
geostationary orbits of the QZSS satellites have been designed so that there is always one satellite above 70-deg in Japan. This dramatically improves position determination in Japanese cities: the three satellites of the QZSS constellation provide much better high-elevation coverage over Japanese cities than the 30 GPS satellites combined (Petrovski, 2003).
RAD Radar airspace applications. RAIM GPS receiver autonomous integrity monitoring. RFID Radio frequency identification. RNAV Aerial navigation. ROV Remotely operated vehicle. RSA Rivest-Shamir-Adleman public-key algorithm. RTCA Radio Technical Commission for Aeronautics. SAGPS Self-Assisted GPS. Rather than access assistance data through a TCP/IP link to a
remote server, or by direct connection to a PC, this approach generates an extended ephemeris on the mobile device itself directly, requiring no access to the internet whatsoever (Mattos, 2008). As a reference, the method uses a recent, if expired, ephemeris for the satellites in view, and revises it by employing the known perturbing effects of the Sun, Moon, Earth’s oblateness, solar flux, etc. In clear skies with unobstructed satellites, there is a slight degradation of the accuracy of this method, as measurements are then good and satellites abundant, but ephemeris is degraded. The aim is to provide standalone ephemeris prediction for PNDs on start-up within 5 to 10 seconds after being switched off for up to 3 days, instead of the traditional 30 second warm-start, which can extend to minutes in urban canyons, or on highways with light poles. Because of the random walk of satellite clocks, this approach does not support predictions longer than 3 days without new ephemeris.
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SBAS Space-based augmentation systems. Augmentation systems with public stake and which use geostationary satellites include North America’s WAAS, Europe’s EGNOS, Japan’s QZSS, Japan’s MSAS, India’s GAGAN, and China’s Beidou-1. QZSS’s main application is to supplement GPS by increasing the number of satellites visible in urban areas and mountainous regions. It provides GPS differential corrections to higher resolutions than MSAS (ICAO, 2012). GAGAN differs from IRNSS, another of India’s regional satellite navigation system. IRNSS’s service area is from longitude 40-deg to 140-deg and the planned accuracy within India is about 20 m (1-sigma) horizontally and vertically. Three of the seven IRNSS satellites will be geostationary and shared with the GAGAN SBAS system. The other four satellites will be divided between two geostationary orbits, inclined at 29-deg and crossing the equator at 55-deg and 112-deg (Sarma et al., 2010). China’s regional Beidou-1 system is currently being upgraded to a full global constellation called Beidou-2 or Compass. It will have 5 geostationary (GEO) satellites, 4 middle earth orbit (MEO) satellites, and 5 inclined geosynchronous orbit (IGSO) spacecrafts. The full Beidou-2 (Compass) system is planned to be operational in 2020 and will have 5 GEOs, 3 IGSOs, and 27 MEOs. Commercial SBAS outfits include OmniStar and StarFire.
SDR Software-defined radio. SIL Surveillance integrity level. SMS Short message service. TTFF Time-to-first-fix in the use of GNSS for finding location. UUID Universally unique identifier. An identifier standard used in software
construction, standardised by the Open Software Foundation (OSF) as part of the Distributed Computing Environment (DCE).
UWB Ultrawide band. V2I Vehicle-to-infrastructure. V2V Vehicle-to-vehicle. V2X The group of common underlying system components of V2V and V2I
communication. VII Vehicle infrastructure integration. VNAV Vertical navigation. VOR Very high frequency (VHF) omnidirectional radio-range. WAAS North America’s wide area augmentation system (see SBAS). WHOIS A query and response protocol that is widely used for querying databases that
store the registered users or assignees of an internet resource, such as a domain name, an IP address block, or an autonomous system, but is also used for a wider range of other information. The protocol stores and delivers database content in a human-readable format.
WiDi Wireless display. WiMAX Worldwide interoperability for microwave access. The interoperable
implementations of the IEEE 802.16 family of wireless network standards ratified by the WiMAX Forum.
WLAN Wireless local area networks. XMP Extensible metadata platform. XMP is an ISO standard, originally created by
Adobe Systems Inc., for the creation, processing and interchange of standardised and custom metadata for all kinds of resources.
ZUPT Zero velocity update.