Lecture 1: Overview of Wireless Standards · 1/23/2006 · In 2000 indoor WiFi localization...
Transcript of Lecture 1: Overview of Wireless Standards · 1/23/2006 · In 2000 indoor WiFi localization...
January 23, 2006 ©KP
RFID Academic ConvocationAUTO-ID Laboratory at MIT
CWINS
Trends in RF Location Sensing Prepared by: Kaveh Pahlavan,
Prof. of ECE and CS and Director CWINS, WPI Presented by: Allan H. Levesque
January 23, 2006 RFID Research Convocation
Outline
Evolution of RF Location Sensing Applications – in Commerce – in Military and Public Safety – in other disciplines
Localization Research at CWINS/WPI – RSS Localization– Accurate Localization Using TOA
January 23, 2006 RFID Research Convocation
Evolution of Localization IndustryProblem of locating soldiers carrying mobile radios was first addressed in World War IIDuring the Vietnam conflict US department of defense lunched a series of GPS satellites to support military operationIn 1990 signals from GPS satellites became accessible to publicIn 1996 FCC required locating mobile phones with in 100 meters in 67% of locationsIn 1997 urban and indoor geolocation started– Military: SUO/SAS project– Commercial: PinPoint, WearNet
In 2000 indoor WiFi localization (Newbury Networks and Ekahau)In 2003 UWB localization for WPANsIn 2004 localization for sensor networksIn 2005 outdoor WiFi localization as a GPS alternatives– Commercial: WiFi positioning in outdoor areas (Skyhook)– Military: Signals of opportunity for disaster recovery (DARPA)
January 23, 2006 RFID Research Convocation
Localization in Commerce
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Typical Commercial ApplicationsAsset tracking in warehouses and hospitalsDispatching and monitoring support staffLocating elderly personsLocating children in public areasEmergency management in hospitalsInvisible fencing of petsTracking golf ballsLocating surgical equipment in operation roomsGuiding visitors in museums Navigating sight impaired personsGPS complement for indoor applications
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Today Everything is a Terminal!
Printer
Scanner
iBook
VCRVCR
Entertainment /Home Networking
Location Sensors
Computer/Communication
Appliances/SecurityControl/Metering
PROGRAM
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Standards and Terminals
1
100,000
10
100
1,000
10,000
MPE
G2
Vide
o
3D G
ames
Ban
dwid
th (K
bps)
(100 Mbps)
(10 Mbps)
(1 Mbps)
Prin
ting
Inte
rnet
Acc
ess
Secu
rity
Syst
ems
Trad
ition
al G
ames
Phon
e A
pplia
nces
Rea
l Aud
io
MP3
Aud
io
Smar
t App
lianc
es
Util
ity M
eter
ing
Cam
cord
er D
V
USB
Con
nect
ors
1000,000 (1 Gbps)
Mul
tiple
HD
TV
802.15.4 WPAN
802.11 WLAN
802.15.3a WPAN
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Localization Accuracy
Mbps1 10 1000.1
Out
door
Fixed
Walk
Vehicle
Indo
or
Fixed/Desktop
Walk
Mobility
WLAN
User Bitrate, Datacom services
WPAN
WiFi Positioning (a few meters indoors)
UWB (a few centimeters indoor)
E-911 (100 meters 67%)
1000
3G Cellular
2G C
ellu
lar
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Accurate Localization in Military and Public Safety
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Typical Applications
Navigation for fireman Navigation for policemenProviding situation awareness for soldiersTracking inmates in prisonsTracking in disaster recovery zones
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SUO-SAS and Urban Fighting
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Localization Everywhere
Robot in rubble Forces inside buildingsInside caves
Urban canyons Unattended ground sensors
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Using Signals of Opportunity
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Localization in other Research Areas
Location-based handoffLocation-based routing in ad hoc networksLocation-based authentication and security
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Localization Research at CWINS/WPI
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Two TechnologiesRSS
– Simple hardware • Accuracy is not sensitive to multipath and bandwidth• Does not need synchronization• Can work with any existing infrastructure and card which can measure power
– For accurate localization • Needs high density of references or long training procedure• Needs more complex and computationally intensive algorithms
– Used for WiFi Positioning by Ekahau, Newbury Networks, PanGo, Skyhook. TOA
– Suitable for accurate indoor positioning• Only needs a few references• Does not need training
– More complex hardware • Accuracy is sensitive to multipath and bandwidth• Needs synchronization of the reference time
– Used in GPS, PinPoint, WearNet, 802.15.3 and 4.
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Two Classes of Algorithms
Tx1Tx2
Tx3
d1
d2
d3x
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AP1 AP2
AP3
AP4 AP5
MH
100 m
ReferencePoint
100 m
GridResolution
Y
X
Distance Based Localization
Pattern Recognition
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RSS Localization at CWINSMicrosoft: Nearest Neighbor algorithm based on signature data collection in a training period.
– P. Bahl and V. Padmanabhan, “RADAR: an in-building RF-based user location and tracking system,” IEEE INFOCOM, Israel, March 2000.
Ekahau: Uses a statistical algorithms for localization with improved accuracy and reduced training data.
– T. Roos, P. Myllymaki, H. Tirri, P. Miskangas, and J. Sievanen, “A Probabilistic Approach to WLAN User Location Estimation,” International Journal of Wireless Information Networks, Vol. 9, No. 3, July 2002.
CWINS: Develops a real time laboratory testbed and a framework for comparactive performance evaluation of localization algorithms.
– M. Heidari and K. Pahlavan, Performance Evaluation of Indoor Geolocation Systems Using PROPSim Hardware and Ray Tracing Software, IWWAN’04, Oulu, Finland, May 2004.
CWINS: Uses channel modeling techniques (Ray Tracing and WiFi channel models) to eliminate the need for training data and improve the performance.
– A. Hatami, and Kaveh Pahlavan, " Comparative statistical analysis of indoor positioning using empirical data and indoor radio channel models," Consumer communications and networking conference, 2006.
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Challenges for TOA Estimation Coverage– Existing knowledge only supports coverage of the total power while
in TOA estimation we are keen on the coverage of the DP. We need more measurement and modeling effort to solve this problem.
Bandwidth– Common belief: increase in bandwidth increases the resolution
therefore UWB is the final solution– Problem: Increase in bandwidth reduces the coverage of the first
path, which means UWB is not the ultimate solution for Indoor Geolocation
UDP– Errors caused by UDP conditions are very large – Detection of UDP is difficult, so we can not avoid UDPs– If we detect the UDP and avoid, coverage become very small
Therefore: we need research in algorithms which operate in UDP conditions and provide good coverage with reasonable bandwidth
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TOA Research at CWINS
Modeling of the behavior of TOA sensors (for NSF)UWB– For high-resolution short distance (for DARPA)
Super-resolution algorithms– For signals of opportunity (with NSF and RCI)
Diversity Techniques– AOA (with Draper Laboratory)– Space and cooperative location estimation (with IWT)
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ConclusionLocalization is one of the fastest growing industries attracting strong research investments from military and commerce Variety of applications are emerging around RSS and TOA based technologies CWINS is the oldest laboratory working in both RSS and TOA technologies with close ties to commerce and militaryLocalization for RFID applications is one the areas of interest at CWINS for future research