Multi-Satellite Intelligence Collection Scheduling · Résumé . Sous l’égide du projet de...

Multi-Satellite Intelligence Collection Scheduling Problem Statement

Jean Berger DRDC – Valcartier Research Centre Prepared For: Joint Intelligence Collection and Analysis Capability Project DRDC – Valcartier Command, Control and Intelligence Section Québec (Québec) G3J 1X5 Canada

Defence Research and Development Canada Scientific Report DRDC-RDDC-2016-R181 September 2016

IMPORTANT INFORMATIVE STATEMENTS This work has been conducted at DRDC – Valcartier under the DRDC Joint Force Development (JFD) 4 Joint Intelligence Collection and Analysis Capability (JICAC) project between mid-July 2015 and March 2016.

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© Her Majesty the Queen in Right of Canada, as represented by the Minister of National Defence, 2016

© Sa Majesté la Reine (en droit du Canada), telle que représentée par le ministre de la Défense nationale, 2016

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Abstract

Under the DRDC Joint Intelligence Collection and Analysis Capability (JICAC) project, this report formulates an intelligence collection tasking problem statement for suitable Canadian Forces (CF) application intelligence domains. Contextualizing intelligence collection based upon environment properties and a high-level resource planning and scheduling framework, a specific intelligence collection tasking problem is defined. Accordingly, a multi-satellite intelligence collection scheduling problem of interest is stated, describing intelligence tasks, collection assets, related respective characteristics, most salient features, attributes and constraints, along with possible pursued objectives. The document also briefly surveys key relevant work from open literature and, identify relevant CF application domains including space-based intelligence, surveillance and reconnaissance (ISR).

Significance to Defence and Security

This report characterizes an important and basic multi-satellite intelligence collection scheduling problem, applicable to space-based ISR and other domains. This work is timely aligned with some concurring DRDC efforts in meeting some CF priority such as persistent intelligence, surveillance and reconnaissance in the Arctic and the North. It namely relates to recent All Domain Air Situational Awareness (ADSA) and compressed Tasking-Collection-Processing & Exploitation-Dissemination (TCPED) initiatives. It paves the way to the development of new science and technology approaches ultimately aimed at providing near optimal intelligence collection for low density, high demand deployable collection assets. The proposed problem statement can be adapted and specialized to suitable collection management use cases to derive, develop and assess a new suite of concepts, decision models and efficient intelligence collection planning algorithms.

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Résumé

Sous l’égide du projet de Capacité de Cueillette et d’Analyse de Renseignement Inter–armées ou « Joint Intelligence Collection and Analysis Capability » (JICAC) de RDDC, ce rapport énonce un problème de planification de tâches de cueillette du renseignement pour des domaines d’applications pertinents du renseignement des Forces Canadiennes. Un problème de cueillette du renseignement fondé sur des propriétés environnementales et un cadre de planification et d’ordonnancement des ressources est présenté. Un problème d’intérêt d’ordonnancement multi-satellite de cueillette est ainsi défini, décrivant des tâches de renseignement, des ressources de cueillette, leurs caractéristiques respectives, leurs principaux traits, attributs et contraintes, ainsi que les objectifs à atteindre. De plus, le document brièvement dresse un sommaire succinct des contributions de la littérature scientifique puis identifie des domaines pertinents d’applications des Forces Canadiennes, notamment pour le renseignement, la surveillance et reconnaissance effectués à partir de l’espace.

Importance pour la défense et la sécurité

Ce rapport caractérise un important problème de base d’ordonnancement multi-satellites pour la cueillette de renseignement applicable au renseignement, la surveillance et reconnaissance effectués à partir de l’espace et autres domaines. Ce travail est parfaitement aligné avec d’autres efforts de RDDC visant les priorités des Forces Canadiennes telles le renseignement en continu, la surveillance et reconnaissance dans l’Arctique, et le nord. Les initiatives récentes « All Domain Air Situational Awareness (ADSA) » et « compressed Tasking-Collection-Processing & Exploitation-Dissemination (TCPED) » en sont des exemples. Il trace la voie au développement de nouvelles approches de science et technologie destinées à une cueillette du renseignement quasi optimale pour des ressources de cueillette déployables de faible densité, en forte demande. L’énoncé de problème proposé peut être adapté et spécialisé à certains cas d’intérêt de gestion de cueillette afin de dériver, développer et évaluer une suite de nouveaux concepts, modèles de décision et algorithmes efficaces de planification de cueillette du renseignement.

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Table of Contents

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i Significance to Defence and Security . . . . . . . . . . . . . . . . . . . . . . i Résumé . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii Importance pour la défense et la sécurité . . . . . . . . . . . . . . . . . . . . ii Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2 Collection Management . . . . . . . . . . . . . . . . . . . . . . . . 2

2.1 Intelligence Collection Tasking . . . . . . . . . . . . . . . . . . . 3

2.1.1 Collection Environment Properties . . . . . . . . . . . . . . . 3

2.1.2 Collection Planning and Scheduling . . . . . . . . . . . . . . . 5

3 Multi-Satellite Collection Scheduling . . . . . . . . . . . . . . . . . . . 9

3.1 Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3.2 Novelty . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

4 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

5 Application Domains . . . . . . . . . . . . . . . . . . . . . . . . . 22

5.1 Arctic Intelligence . . . . . . . . . . . . . . . . . . . . . . . . 22

5.2 CF Projects from the Defence Space Program . . . . . . . . . . . . . . 23

5.3 DRDC Initiatives . . . . . . . . . . . . . . . . . . . . . . . . . 24

6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

List of Symbols/Abbreviations/Acronyms/Initialisms . . . . . . . . . . . . . . . 33

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List of Figures

Figure 1: Tasking and CCIRM as the pivotal of TCPED. Reproduced with permission from SPIE. The original can be found in [2]. . . . . . . . . . . . . . . 2

Figure 2: Intelligence Collection Environment – Complexity Spectrum . . . . . . . 4

Figure 3: Planning and scheduling relationship. . . . . . . . . . . . . . . . . . 6

Figure 4: RADARSAT-2. Reproduced with permission and courtesy of the Canadian Space Agency and IEEE. The original can be found in [3]. . . . . . . . . 10

Figure 5: Sun-synchronous Orbit. Reproduced with permission from Wikipedia. The original can be found in [4]. . . . . . . . . . . . . . . . . . . . . . 12

Figure 6: Arctic—exercise Sovereignty, ensure Defence, Security, Safety—inspired from [59]. Partly reproduced with permission from the Office of the Oceanographer of the Navy. The original can be found in [59]. . . . . . . . 23

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1 Introduction

The explosion of collection assets or information requests to support information need defined by intelligence analysts call for efficient and effective intelligence asset collection allocation. Based on an intelligence collection scoping study [1] recently carried out under the DRDC Joint Intelligence Collection and Analysis Capability (JICAC) project to rapidly develop a coherent understanding and characterization of the collection problem dimensions, a relevant intelligence collection decision problem is defined for low density high demand collection assets.

This document presents an intelligence collection tasking problem statement targeted to suitable Canadian Forces (CF) Intelligence, Surveillance and Reconnaissance (ISR) application domains. Environment properties contextualizing a collection tasking problem are first introduced. Selecting basic properties, the collection resource allocation problem is described following a high-level resource planning and scheduling methodology framework. A multi-satellite intelligence collection scheduling problem instance targeted to space-based ISR application domains is then stated, depicting more explicitly salient feature characteristics and attributes of interest. Key elements such as intelligence requests and tasks, collection assets, constraints and pursued objectives are specifically introduced, while departing from traditional approaches bringing in the notions of compound task, quality of service, partial task completion and probability of success. Related work from the open literature and potential ISR application domains are briefly exposed as well for reference purposes. This work has been conducted under the DRDC Joint Force Development (JFD) 4 Joint Intelligence Collection and Analysis Capability (JICAC) project between mid-July 2015 and March 2016.

The report is outlined as follows. Chapter 2 contextualizes the collection tasking problem, defining environment properties while introducing a high-level planning framework. A problem statement of a multi-satellite intelligence collection scheduling problem instance is given in Chapter 3. It highlights main problem features and proposed novelties. Chapter 4 briefly outlines related work from scientific literature whereas Chapter 5 summarizes anticipated application domains of interest. Finally, a conclusion summarizing key outcomes and proposed way ahead is given in Chapter 5.

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2 Collection Management

Collection management is the process of converting intelligence-related information requirements into collection requirements, establishing priorities, tasking or coordination with appropriate collection sources or agencies, monitoring results, and retasking, as required. It is responsible for the collection coordination and information requirements (CCIRM) portion of the Direction phase/function as contextualized by the ‘Tasking’ component of the classical Tasking-Collection-Processing & Exploitation-Dissemination (TCPED) intelligence cycle depicted in Figure 1. The collection component of TCPED refers to collection execution and therefore, is not explicitly part of collection management per se.

Figure 1: Tasking and CCIRM as the pivotal of TCPED. Reproduced with permission from SPIE.

The original can be found in [2].

Overall collection is ultimately aimed through resource management at satisfying request for information (RFI) and then, prioritized information requirements tasks bridging the gap between information need (task-driven) and information gathering. Typically defined by intelligence planners, priority information requirements (PIRs) describe general intelligence need. PIRs are then further articulated and broken down into essential elements of information (EEIs), capturing more detailed information requirements. PIRs and EEIs notions are exploited by information collection planners to relate commander’s intent with collection asset tasking (collection request) and situational awareness. The EEIs can be further decomposed at different levels of granularity, instantiated, refined or reconfigured. Building from hierarchical information need decomposition into prioritized intelligence requirements to reflect commander’s critical information


requirements and intents, collection management also relies on maintaining persistent collection management awareness to the collection manager and analysts while performing collection tasking.

Collection management (CM) awareness:

Collection management awareness is typically supported through the following functions:

Visualization (intelligence requirements, request for information structure, intelligence requirements task execution status & traceability, pedigree, lineage, ISR picture, mission state estimation, risk, collection asset visibility and readiness/status, sensor-task performance profiles, and cost).

Monitoring (ongoing/upcoming collection plan execution status).

Analysis (comparison, plan execution simulation, ‘what-if’, partial goal achievement, asset readiness, forecasting/prediction).

Simulation (situation, plan, outcome projections).

Collection Tasking:

Tasks are naturally determined and dynamically modulated by action outcomes, new evidence analysis, emerging goals or state transition estimations demanding proper dynamic resource management (collection tasking/planning/scheduling) and coordination. This leads to the task-asset assignment problem matching best suitable asset capability to individual task’s information needs to maximize a specific objective such as value of information or expected information gain. Efficient task-asset assignment remains nonetheless a hard problem to solve as high demand typically placed on low-density available assets far exceeds the inventory level making computational complexity a real challenge. The problem is further exacerbated by assets owned by various stakeholders competing for tasks in time-constrained uncertain environment.

The remainder of the document primarily focuses on collection management from a collection tasking perspective, attempting to further define the problem for organic collection asset allocation. Information-gathering resource allocation targeted to information retrieval tasks or queries from various sources has been deliberately ignored for complexity purposes and will be considered in the future under separate investigations.

2.1 Intelligence Collection Tasking

2.1.1 Collection Environment Properties

Basic problem environment properties characterizing collection tasking problems may be generally described along various dimensions or properties as shown in Figure 2. Such environment properties may be summarized as follows:

Fully observable vs partially observable state (shared information: Resources, plans).

Deterministic vs non-deterministic (stochastic) (e.g., sensing/collection action outcome).


Static vs dynamic (involve a single or multiple time periods).

Episodic vs sequential (current decision impacts future actions):

The episodic term often refers to an open-loop setting. An open-loop system ignores an explicit feedback outcome or a world/system state transition resulting from a sensing or a plan execution action, whereas an ‘open-loop with feedback’ or a closed-loop setting refers to a sequential process where future decisions depend on current ones.

Discrete vs continuous.

Close-world vs open-world assumption. An open world refers to a complex environment with a limited ability to perfectly predict behavior models, events, decision and outcomes.

Figure 2: Intelligence Collection Environment – Complexity Spectrum

Depending on problem domain complexity, these environment properties give rise to multiple technical challenges to efficiently support collection tasking to successfully enhance persistent situational awareness (state estimation). From a general planning perspective these include but are not limited to:

Gap reduction between:

Information-gathering (collection planning and execution) and, information need and fusion, coupling sense-making (SM) and resource allocation (RA) tasks.

Planning (tasking) and execution (collection) monitoring.

Resource allocation combinatorial complexity:

Managing resource contention and combinatorial complexity of low density/high demand asset allocation problems in which information request far exceeds platform capability and inventory level. Mixed-initiative planning further exacerbates inherent complexity.

Continual centralized and distributed planning in dynamic uncertain environment:


Dynamic planning represents a key area imposing stringent near real-time requirements to support dynamic tasking and retasking, sensing and cross-cueing/tipping, contingency planning and anytime/adaptive behaviors respectively. As a natural extension to the centralized setting, a key distributed planning (information-gathering) component aims at designing adaptive coordination mechanisms and approaches leading to suitable/desirable team behaviors to achieve near optimal global resource allocation. It typically involves a mixture of heterogeneous stakeholders and collection asset/capability owners.

Uncertainty management:

The design of suitable uncertainty management mechanisms and strategies as well as its integration in the development of semi-automated adaptive and responsive decision support systems and behaviors. Uncertainty management plays a key role in handling, ignorance, randomness and imprecision in a variety of situations including partial observability (sensor performances), exogenous event occurrences, situational awareness (state estimation), and risk management.

Constraints integration:

Resource contention, heterogeneity.

Bounded sensitivity, rationality and reactivity.

Information-bounded situational awareness (private vs shareable information), information-bounded planning (private/shared information).

Service time-windows.

Sensor network settings (fixed and mobile ad hoc sensor networks, resource allocation, communication network latency, bandwidth and cost).

Constraint multiplicity and diversity attached to various application domains.

Human-computer interactions.

A first step consists in naturally managing problem complexity by focusing on an open-loop collection environment and a centralized single episode decision problem defined over a given time horizon.

2.1.2 Collection Planning and Scheduling

Intelligence collection tasking of low-density, high-demand collection assets can be naturally mapped to a resource (namely, collection asset) allocation problem using a high-level planning and scheduling process. Planning and scheduling can generally be either integrated where both problems are solved simultaneously or serialized where both problems are executed sequentially starting first with planning. This relationship is schematically sketched in Figure 3. The selected approach or process is naturally driven by problem complexity. Collection planning per se primarily focuses on action tasks to be planned and resource to be allocated to successfully satisfy task requirements ignoring the precise time tag attached to resource utilization. The combinatorial element still unspecified from task-sensor package plan instantiations primarily remains over specific temporal resource assignment. Tasks are problem domain-dependent and may be partially defined or already given. On the contrary, scheduling assumes that task generation,


action plans and required resource have already been predetermined from the planning stage, and rather focuses on resource temporal ordering over admissible collection time intervals (e.g., opportunities), indicating more accurate resource deployment time (or interval) and possibly in some cases, assignment duration.

Figure 3: Planning and scheduling relationship.

Despite specific intelligence tasking decomposition problem setting considerations, a high-level planning process can generally be broken down as follows:

Input:

Set of task requests:

Requests or orders represent priority intelligence requirements organised in a structure (e.g., a tree) reflecting information need. They may derive from known mission scripts capturing task of interest structure (including essential elements of information and indicators to measure task completion/success).

Set of collection assets and supporting resources:

Sensor, system and platform repositories.

Database of technical parameters for collection assets of interest (e.g., SAR, EO/IR and Thermal IR sensors).

Set of supporting resources (e.g., ground stations).

Collection tasking objective

Constraints set:

Task, temporal, budget, capacity (itinerary, route/run, collector) performance.

Preprocessing:

Task Request:

Task generation:

A request defines an area of interest, namely, a target area (spot) or a polygon area to be covered (task). The area of interest can be further broken down in subareas (coverage subtasks). In turn, subareas are generally decomposed in strips, mapping partial task/subtask coverage generated from collection asset visits. Each strip is mapped to a new task or subtask. Therefore, task generation is closely related to the strip generation process which in turn is asset-dependent.

It is worth mentioning that for a group of homogeneous collectors following the same path, a large area of interest (polygon) may generally be partitioned in disjoint strips (and related subtasks) for a given resolution. This occurs for


homogeneous platforms composing a satellite constellation following similar orbits and separated by small time intervals over repeated cycles. In that case, homogeneity is calling for a natural mapping between strip and subtask as a matter of convenience. But in general, collection assets having dissimilar trajectories (e.g., satellite tracks) or characteristics (heterogeneous) are associated with their own suite of path-dependent disjoint strips. As a result, overlapping strips are likely to emerge when considering collection asset trajectories altogether. In that scenario, the real gain in matching subtasks to a fixed, arbitrary and common strip pattern for all assets is highly questionable. The reason lies on the fact that many collectors are unnecessarily constrained to look in regions with degraded observation quality, given unsuitable geometry constraints imposed by a fixed strip pattern. An alternate opportunity-dependent (satellite pass) strip-subtask mapping construction scheme would rather exploit best or favorable geometry constraints to maximize specific collector image acquisition and therefore, overall collection quality.

Task clustering/splitting (preprocessing):

Task aggregation through clustering/merging to reduce computational complexity. Multiple tasks that can be concurrently serviced by a single collector may be clustered in a single task.

Collection Plan generation:

Sensor capability-task matchmaking:

A task (related requirements) is matched to a suitable sensor type/class (or sensor package/combination class) capability. Platform selection for a specific role depends on a number of factors including:

Capability of the sensor, area to be surveyed.

Frequency of observation required (repeat cycle).

Sensor coverage.

Availability.

Configuration, resolution.

World state variables dependencies.

Uncertainty, expected probability of success or quality of service.

Impact of adversarial planning, obstacles.

Weather.

Contingencies.

Kinematics.

Terrain obscuration, effective concealment, camouflage and deception.

Risk, threats.

It contributes to generate feasible candidate task-asset pairings. For IMINT or GEOINT, National Imagery Interpretability Rating Scales (NIIRS) reflecting


information level required or expected to satisfy an imaging task may be exploited to facilitate credible feasible sensor packages matching.

Collection opportunity generation:

Task collection (namely, imaging/observation and downlink) opportunity calculation allows expanding plan generation to the next level of details, namely, scheduling. Such computation accounts for collector field of view (footprint, track/corridor visibility). Opportunity generation identify all feasible task-collector schedules accounting for task visibility and service capability periods (e.g., imaging time intervals for servicing a task—strip coverage), including relevant attributes such as task identifier, resolution (spatial, spectral) and related waveform, look/incidence angle, collector orbit/tour and related identifier, and measures of performance/effectiveness (e.g., task coverage proportion for that single opportunity, probability of success or task completion level of confidence, energy consumption, image acquisition cost, etc.). Legal collector moves may be represented over time using network flow capturing possible collector actions/decisions (e.g., specific task imaging) linked to kinematic behavior and environment geometry. The same notion of opportunities applies as well to required supporting resource, playing a role in completing collection (e.g., image downlink for specific ground station network nodes).

Collection Plan evaluation:

Various task-dependent measures of performance may be used to assess and qualify an individual feasible plan (collector allocation configuration) for a given task. In task imaging, this may range for example from value, combined area coverage, resolution, look angle, resource/energy consumption, execution and completion time, imaging cost, probability of success, expected residual uncertainty or error on state estimation, or risk exposure up to any combination of them.

Collection Plan Selection:

Plan selection consists in performing collection asset-task allocation. It may range from basic satisficing solution computation to full planning/scheduling optimization. A mix of solvers exploiting a variety of exact and approximate algorithms, as well as heuristic or meta-heuristics methods may be used to ultimately propose or recommend one or many possible solutions to the user.

Output: collection asset-task allocation (collection plan, including schedule).

The proposed open-loop collection scheduling space-based ISR problem to be stated in the next Chapter as well as any future problem specification will be based on the above high-level breakdown structure and solved accordingly. A bias for an initial open-loop setting is mainly driven by problem complexity.


3 Multi-Satellite Collection Scheduling

3.1 Statement

A high-level problem class of interest is the centralized open-loop multi-satellite collection tasking problem. Focusing on imagery collection (image acquisition) scheduling, downlink scheduling has been deliberately overlooked. Both scheduling problems have been decoupled and will be handled separately to reduce overall problem complexity. Multi-satellite collection tasking can be stated as follows: given a set of information requests (areas of interest to be observed) properly translated in weighted tasks, the problem consists in allocating collection assets to imaging observation tasks over a predetermined time horizon to optimize single or multiple objectives. Alternate problem input and/or characteristics include a set of collection assets and supporting resources (e.g., ground stations), a collection tasking objective, and a set of constraints. Constraints may relate to mission, task, operational, collector, supporting resource, communication, capacity, temporal, itinerary and cost considerations respectively. Requests are assumed to originate from requests for information, priority intelligence requirements derived from commander’s critical information requirements, and prior situation knowledge (e.g., Intelligence Preparation of the Battlefield – IPB, ISR picture). These are conveyed through an existing collection management process and supporting tool, as depicted in Figure 1. A deterministic and/or a limited stochastic (observation outcome uncertainty) environment setting is assumed to prevail, subject to resource contention (low-density, high demand assets) and high combinatorial complexity. Abstract problem task decomposition is further assumed to be known in advance, reducing ‘collection tasking’ to a scheduling problem. Collection planning per se covers in counterpart a larger problem spectrum, including additional resource allocation and complexity considerations, ranging from partial to complete task/plan generation, possibly taking into account multi-level (abstract) sensing actions.

The multi-satellite imagery collection scheduling problem can be defined through the 5-tuple <R, C, Sppt, Obj, A, M, CONS > stated as follows:

R: Set of requests.

A mission script based on a suitable military business process and prior knowledge (e.g., IPB, doctrine) is assumed to define a generic task structure (typically a tree) of interest, including essential elements of information (EEI) and indicators to evaluate task goal achievement. Task requests and relative value and/or priority are specified all the way down to the collection task level. The current problem description essentially focuses on the leaf nodes of the task tree structure as task goal achievement or evaluation ultimately stems from base-level collection task nodes up. A leaf node ultimately leads to an intelligence request.

An imaging request (or order) refers to a specific area interest (AOI) to be surveyed or covered by a spaceborne sensor and can be de decomposed in a single (primitive) or multiple (compound) survey tasks trough a task generation process. An AOI is either defined as a spot (small target) or a polygon referring to a primitive and compound task respectively. A polygon can be decomposed in multiple strips corresponding to satellite swaths intersecting the AOI during a given orbit. A strip defines a simple or primitive task composing the request (task generation) to be covered for

http://fas.org/irp/doddir/army/ioac/ipb.htm


a single satellite. A spot is assumed to be covered by a single strip. Typical strip coverages by RADARSAT-2 satellite swaths are pictured in Figure 4 for various sensor waveforms. Shifting attention from requests to tasks, it is worth mentioning that a single or compound task may possibly be further clustered (aggregation) with alternate request tasks or split for convenience purposes when observing similar regions (preprocessing) or aiming to provide computational complexity reduction through additional decomposition during problem-solving. The latter procedure occurs during the preprocessing stage introduced in Section 2.1.2. Therefore, tasks may be independent or aggregated, clustered or ultimately, partly covered (partial task satisfaction). Note that beyond typical large area coverage survey tasks, a complex request may be defined as multiple independent primitive tasks. For instance a mission request may include an abstract (compound) task representing a set of subtasks to be concurrently or sequentially executed as exemplified by the sequence of detection, location, tracking, classification, identification, and confirmation subtasks typically required for a complex intelligence collection request. Primitive tasks may involve basic activities such as survey (surveillance, reconnaissance), detection, tracking, identification, confirmation, monitoring, search, and outcome assessment.

Figure 4: RADARSAT-2. Reproduced with permission and courtesy of the Canadian Space

Agency and IEEE. The original can be found in [3].

A task structure derived from a request for information (RFI) along with its related attributes and requirements may be defined as follows:

Task structure (template)

Customer

Information need –driven

Priority, associated weights/nominal relative value (value of information).


Time-dependent RFI (e.g., time sensitive targeting).

Task network (directed acyclic graph—task structure decomposition) derived from EEIs:

Specification (task interpretation formalism).

Primitive/compound (non-primitive/abstract/decomposable—mono/stereo).

Information dependencies (gathered information impacting/feeding other tasks—e.g., area of interest (AOI) overlap).

Type/goal (e.g., detect, locate, track, classify, identify, confirm, monitor, search, assess outcome, surveillance/survey, reconnaissance).

Mission importance/profit/priority/weight of the selected observations and downloads.

Indicators, measure of performance (MOP) and measure of effectiveness (MOE) for goal achievement (e.g., quality of service, probability of detection, resolution, uncertainty, value of information (VoI) measure—e.g., National Imagery Interpretability Rating Scales (NIIRS)-like along with related thresholds).

Lifecycle: Single/transient: Time windows; persistent/recurrent: Frequency.

Collection requirements:

Demand: single (pre-emptive/not) vs multiple (sequential/split collection delivery), NIIRS/resolution mode (beam/waveform), polarization, resource capability requirements.

Mono/stereo: single or multiple possible observations per task (e.g., detection, confirmation).

Duration.

Periodicity—Repeat cycle (period) if required.

Observation quality: observation angle or acceptable incidence angle interval (min/max off nadir angle).

State conditions (e.g., environmental prerequisites, luminosity—sun orientation).

Direction (ascending/descending/no orbits).

Imaging completion time window (customer requirements).

Task request probability distribution.

C: Set of collection assets (collectors).

Targeted collection assets of interest primarily involve spaceborne platforms or spacecraft vehicles, namely Earth observation satellites, focusing on remote sensing tasks, even though this group of assets can be easily extended to any alternate deployable systems (e.g., airborne vehicles) or intelligence sources. Accordingly, most remote sensing platforms are designed to follow a north-south trajectory [4] depicting near-polar orbits. When the course of a satellite is combined with Earth's rotation, fully extensive Earth’s surface coverage is made feasible on a


periodic basis. Targeted satellite orbits are assumed sun-synchronous (or heliosynchronous), imposing successive orbital passes to occur at the same time of day, therefore providing the opportunity to possibly cover each specific area at a given local time. As a result, relative sun’s position remains the same as the satellite passes overhead at a specific latitude during a given season. A sun-synchronous orbit is shown in Figure 5. Such satellite trajectory which allows maintaining consistent illumination conditions suitably enables valuable image acquisition of a particular region over a given period. This constitutes a significant advantage in image-based change or anomaly detection analysis or, in composing multiple successive images, as they do not need to be revisited and corrected to account for various illumination conditions. However, in order to nearly maintain the same local time on a given pass, the duration of a low half-orbit period must be reasonably short. The half-orbit on the Sun side typically revolves around 50 minutes, a period showing small variations in local time. Imaging opportunities are limited, as a satellite target overhead passage occurs only once per orbit. For the sun-synchronous orbits used by most Earth observing satellites, a typical revolution is on the order of 90 minutes.

Figure 5: Sun-synchronous Orbit. Reproduced with permission from Wikipedia.

The original can be found in [4].

Collection assets of interest include a mixture of multiple heterogeneous and homogeneous satellite constellations (commercial/non-commercial) possibly involving a variety of geometry configurations and sizes (e.g., small and medium size) as well as a wide spectrum of sensor modality, resolution and platform agility (platform manoeuvrability along specific directions for observation flexibility). It is assumed that platform payload typically consists of a single sensor, either optical (e.g., panchromatic, hyper/multi -spectral, infrared) or radar (e.g., synthetic aperture radar – SAR). Collection asset description and related attributes may be summarized as follows:

Collection Assets (collectors)/Intelligence sources:

Sensors generate observations of phenomena and are mounted on platforms which provide required durability, mobility and communication capability requirements. An “asset” is a platform which contains one or more sensors. Assets are naturally heterogeneous (offering different capabilities, under operational and environmental conditions) and only suitable for particular tasks.


Platforms:

Multi-level (tactical, theatre-level, strategic/national).

Configuration—platform and sensor package.

Platform payloads (sensor suite, multiplicity; resource).

Realm (air, land, sea).

Autonomy/endurance.

Performance (e.g., range, endurance, altitude, speed).

Mission type fitness (e.g., surveillance, reconnaissance, target acquisition).

Capability.

Agility, mobility, availability.

Frepower, landing and takeoff, communications, vulnerability and survivability.

Energy budget, bounded number of observations per period, memory storage.

Communication bandwidth.

Sensors:

Multi-level (tactical, theatre-level, strategic/national).

Types: synthetic aperture radar (SAR), electro-optical (EO) and thermal and infra-red (IR), automated identification System (AIS).

Phenomenology and modalities (e.g., acoustic, optical, infra-red).

Visibility (field of view/footprint), capability, range, mode, number of bands, min/maxoff nadir angle, resolution, swath width, parameter configuration, spectral and spatial resolution sensors (beam waveforms, mode, bands) determining swath width.

Availability, competence/reliability, vulnerability.

Percepts/observables/outcomes—phenomena detected (type and spectrum).

Performance sensitivity vs operational conditions:

o Observation model (likelihood function, belief net—multi-level):

Probability of correct observation, false-alarm rate.

State/environmental conditions (clouds, weather forecast)—conditional probability distribution.

Sppt: Set of supporting resources—typically, ground stations

Image downlink scheduling is ensured through a ground station network and/or a geosynchronous communication satellite extended network. Data can be sent to the ground either when the satellite passes over a ground station or via geosynchronous communication satellites. Ground station windows are as limited as any other target, and suitable communication satellites are only


available when not servicing higher priority tasks. Ground station and relay nodes attributes or parameters include locations, mask (maximum communication range), communication bandwidth and channels, and availability. The downlink scheduling problem is usually solved separately from image acquisition.

Obj: Collection tasking objective

The open-loop multi-satellite collection scheduling consists in determining a set of collection asset-task allocation plan/schedule to optimize a single (e.g., expected value of information collection task maximization) or multiple expected objective/cost/utility functions (e.g., weighted sum or vector) over a given time horizon defining a single epoch/episode. Typical ISR mission tasks supporting persistent situational awareness include survey, monitoring, tracking, search, detection, classification, identification, confirmation and outcome assessment. The collection tasking objective function captures a suitable mission-dependent measure of performance. It is also characterized by the nature and diversity of mission tasks under consideration, related task types and respective values, task-dependent quality of information/service collected (partial completion) and related parameters such as quality of service task requirements and observation probability of success. Single or combined measures of performance characterizing value of information may be defined in terms of service level (task value throughput/demand satisfaction, fairness over task demand, lateness and information age), overall resource and energy consumption, risk exposure, imaging cost or alternatively, expected coverage (survey task), kinematic error covariance, mean-square error, or entropy (tracking task), expected information gain (entropy/uncertainty) or information divergence (state estimation for classification/identification task). Multiple objectives settings may involve a scalarized or aggregated function (e.g., a weighted sum when relative importance may be determined), a lexicographic order scheme defining a relative priority over pursued goals, or a vector of functions purposefully formulated in order to describe the Pareto front of non-dominating best solutions.

A: Action space/set:

The domain of control/information-gathering actions at planner/scheduler’s disposal includes:

Sensor: sensing actions/controls

Mode (waveforms/beams and bands), polarization, and other control actions impacting range/spectral resolutions. Feasible action might be state-dependent.

Actuator

Platform control action to facilitate sensor orientation (actionable camera/antenna pointing angle along some rotation axes—roll pitch, yaw).

User-driven/actionable or predetermined periodic activity patterns such as maintenance and energy management.

M: Collection sensor package capability—task matching

The matching process is aimed at finding feasible sensor package/resource capability and task requirements pairings. Sensor-task feasibility and assessment can typically be achieved through


semantic matchmaking, knowledge-based reasoning and/or classification analysis. Matching depends on many factors taking into account a variety of elements including:

Durations: set-up, service and lead/execution time.

Cost.

Asset capability (source [5]):

Sensor range effectiveness.

Beam waveforms (resolution modes), polarizations, bands technical characteristics.

Day/night operations.

Reporting timeliness.

Geolocation accuracy.

Durability, sustainability, vulnerability

Environment task and requirements:

Operations conditions, task coverage, impact of adversarial planning, obstacles, weather, contingencies, kinematics, terrain obscuration, effective concealment, camouflage, and deception, risk, threat activity and behaviors.

NIIRS level or grade (imagery collection)/resolution mode.

Coverage.

Duration, imaging time windows.

Sensor requirements: conditions:

Optical: weather, energy (sun visibility).

Target visibility and look angles.

Beam waveform (resolution), polarization, band.

Performance profile:

Feasible sensor-task performance model—sensor-task assignment quality/score.

Intelligence source performance profile—observation models.

Collection probability of success

CONS: Constraints set

Constraints set are defined along three separate dimensions, namely, task, resource and temporal.

Task:

Task precedence (e.g., survey, detect, track, identify and confirm sequence).

Single (successful observation assumption – probability of success = 1) or multiple observations/visits per task (e.g., detection, confirmation).

Pre-emptive or not.


Maximum imaging acquisition cost / energy budget / per task and overall, taking into account imaging cost per service provider.

Area coverage/probability of detection/entropy thresholds, fairness between customer requests.

Basic task collection requirements.

Resource:

Asset inventory.

Task visibility (per orbit).

Satellite platform capacity (per orbit):

Payload.

Energy budget/autonomy—task imaging/observation, downlink and transition (e.g., slewing/moving) consumption.

Endurance.

Memory storage (max on-board storage): limited on-board data storage. Images are stored in a solid state recorder (SSR) until they can be sent to the ground.

Number of observations: maximum number of sensor openings.

Thermal: maximum satellite imaging time per orbit. Bounded average satellite imaging time over orbits.

Communication (uplink, downlink): bandwidth, communication cost rate.

Itinerary constraint (maximum/average service time over a given period, utilization/deployment):

Sensors interference or sensor mixes contingencies.

Environment: weather conditions:

Cloud cover. Some sensors cannot see through clouds. Not only do clouds cover much of the Earth at any given time, but some locations are nearly always cloudy.

Routine operations:

Periodic maintenance, re-charging batteries, on-board data processing, orbit corrections, uplink.

Conflicting opportunity over single platform utilization due to contention between observation opportunities (e.g., mutual exclusion of competing concurrent sensing actions over waveforms/beam modes).

Temporal:

Set-up time: startup, shutdown, recovery, altitude stability, processing, replanning.

Transition time: slewing (rates)/moving duration to switch between consecutive observation angles from a predecessor to a successor opportunity task. Transition between look angles can be achieved through instruments mounted on motors that


can point either side-to-side (cross-track), forward and backward along the track, or rotate to point their instruments in any direction (agile satellites).

Time windows/intervals: availability/serviceability:

Service—task imaging.

Dissemination—images downlink (image delivery to customer for consumption).

Visibility—task opportunity visibility for imaging, or downlink to specific ground station network nodes.

Information age—maximum delay between observation and download.

Scheduling time horizon (e.g., 24 hours).

Based on requests, task structure and resources input, the problem consists to determine an intelligence/information collection plan (sensor-task resource allocation) optimizing single/multiple objectives. More technical details on a specific problem instance derived from the above statement may alternatively be found in [6].

A variety of decision problem variants may be further derived and specialized requiring additional components (e.g., state space model) depending on selected planning model abstraction level (granularity and scale), environment properties (stochastic/deterministic, static/dynamic, multiple episodes), constraint complexity (e.g., sensor network heterogeneity, connectivity, reliability, communication, scenario-specific contingencies, bounded rationality/reactivity/sensitivity) and decision problem setting (centralized/decentralized; cooperative/semi-cooperative/self-interested agent behaviors and coordination). Decision problem environment complexity and related assumptions are naturally very domain-dependent.

3.2 Novelty

The envisioned problem includes many new additional features and finer elements granularity that primarily remain overlooked or ignored in open literature. Assuming a multi-satellite collection scheduling problem in conditions of low-density, high-demand collection assets, new features include collection asset diversity and agility, task abstraction, a more inclusive objective and more constraint diversity. The proposed problem definition embraces single as well as multiple mission contexts concurrently. It involves abstract intelligence gathering task generalizing single target areas (spot) focus to include large area coverage explicitly while considering multiple or virtual heterogeneous satellite constellations departing from traditional homogeneous settings. Platform agility will also be addressed but separately, given its additional complexity. The proposed approach distances itself from the old-fashioned view in many respects, considering a weighted task decomposition structure combining composite or abstract requests capturing new spatial and temporal dependencies, reflecting more realistic mission complexity and relaxing mutual independence and separability assumptions. It captures notions of partial task execution and probability of success moving away from unrealistic assumptions on orderly action execution or deterministic outcome. It revisits the concept of task priority utilization to reinstate its primary intent, that is to reinforce its use as as a conflict resolution mechanism rather than a strongly biased myopic priority-based policy to arbitrarily impose task


partial ordering, in order to reduce high complexity demand (low density high demand assets) while preserving as much as possible free exploration of the search space for high quality solutions. The envisioned problem objective intends to capture task performance beyond usual area coverage and include multiple measures of performance, introducing quality of information/service which account for success uncertainty to enhance collected information value. Finally, a variety of new additional realistic constraints are captured to qualify desirable solutions from new temporal task precedence and capacity constraints to multiple measure of performance outcome (thresholds) requirements.


4 Related Work

A basic introduction to the satellite scheduling problem and its premises is described in [7]. Related work includes contributions pioneered by Lemaitre et al. on the satellite planning/scheduling problem. An early survey is given in [8] and [9]. Based upon a low density high-demand collection assets premise, the general problem is known to be computationally hard (requiring exponential resources or run-time). For convenience purposes, early work mainly focused either on single sensor satellite or small and homogeneous constellation problem settings. In most reported cases, it predominantly deals with simple target observation tasks depicted as ‘spot’ areas, and design new task clustering and preprocessing strategies in order to mitigate computational complexity. A more contemporary comprehensive technical survey may be found in [10], [11] and [12] respectively. A general high-level overview of recent open literature contributions is also briefly summarized in [6].

Open-loop satellite planning/scheduling has traditionally been connected to knapsack, time-constrained longest path, coverage path planning, constraint satisfaction, graph-based and mathematical programming problem formulations respectively as reported in [11], [12]. Coverage path planning [13] is naturally well-suited for satellite surveying tasks. But most research contributions largely assume perfect coverage and sensing capability (no false alarm), as well as unbounded resources. A recent survey on coverage literature is reported in [13]. Accordingly, Vasquez et al. [14], [15] and Wolfe et al. [16] formulated satellite scheduling as 0–1 knapsack problem under limited constraints. Original constraint satisfaction problem formulations and variants have been alternatively proposed by Lemaitre et al. [17] and Verfaillie et al. [18]. Graph-based approaches using directed acyclic graph model [19], [20] and graph coloring [21], [22] have also been exploited to handle satellite scheduling. Finally, many approaches relying on a mathematical programming framework has been developed for satellite scheduling, first ignoring key memory and energy capacity constraints, as reported by Benoist et al. [23], Habet et al. [24], [25], [26] and Lemaitre et al. [9]. Building from the original work, Liao et al. [27], [28], Lin et al. [29]–[32] and Marinelli et al. [33] extended problem modeling introducing time-indexed integer programming formulations. Alternate integer programming models exploiting network flow [34]–[37], and longest path problem with time windows [38] formulations have also been suggested.

Competing with the emergence of open-loop satellite scheduling problem formulations, a variety of related problem-solving approaches and variants ranging from operations research, artificial and computational intelligence techniques to heuristic methods have been developed and or adapted. A brief overview is summarized as follows. Most popular metaheuristic methods proposed so far and mainly derived from problem-solving mechanisms and principles inspired from general phenomena and specific domain knowledge, include tabu search [39], [35], [30], [32], [14], [22], bio-inspired search such as genetic algorithms [40], [41], [16] and ant colony [42]–[44], local search [17], [18], hill-climbing and simulated annealing [45], [46]. Greedy algorithms[47], [48], [9], iterative and constructive solution methods [49], [50], [38] and other heuristics have concurrently been developed, while Lagrangian relaxation heuristics were used as approximate problem-solving procedure [35], [29], [32] and [33]. In counterpart, advocated exact methods include mathematical programming techniques (interior point, simplex) and, branch and bound algorithms [47], [48]. The Russian Doll search algorithm [23], [48], [51] is a branch and bound variant in which single search is replaced by multiple searches over nested subproblems,


exploiting the outcome of individual searches to better estimate lower bound on partial solution quality when solving larger subproblems. Alternatively, the use of dynamic programming techniques has been reported in [38] and [9].

Among most promising avenues to solve open-loop large scale problem instances, an operations research-based approach (mixed-integer programming) has recently been proposed by Nelson [10] exploiting task aggregation preprocessing and a column generation technique. Using iteratively a Dantzig-Wolf problem decomposition scheme on multiple scales, the proposed approach exploits task allocation aggregation before solving a set partition master problem concurrently coupled to longest path subproblems. Nelson advocated the column generation integer programming technique to ultimately solve the large scale satellite collection scheduling problem. The approach mainly borrows from operations research to tackle large vehicle routing problem with time windows (VRPTW). In that setting, the satellite scheduling problem is reduced to a constrained VRPTW in which satellites referring to vehicles have limited visibility on customer tasks, and orbits correspond to periodic vehicle tours, subject to a variety of task and capacity constraints. The three-step procedure combines task clustering to aggregate tasks to reduce main-scale problem complexity, column generation problem-solving to handle a simpler upper-scale scheduling problem allocating single satellite to a cluster, and then, local main-scale task scheduling at the cluster level. In the last step, clusters are derived from computed satellite-cluster upper-scale assignment solution, and resulting solution characteristics are further propagated as constraints at the main-scale task scheduling cluster level. However, the proposed approach suffers from a single step clustering process preferred to an iterative one, unnecessarily biasing search and casting some doubts on solution optimality, relegating the suboptimal method to a sophisticated heuristic. This is typically exemplified in cases involving a constellation of satellites following similar tracks slightly delayed over time, making single step clustering intrinsically difficult and questionable, as closely related opportunities that can be easily exchanged among probe path solutions are simply ignored. Furthermore, the resulting upper-scale decision model focusing on single satellite allocation per cluster is likely to meet major challenges in computing polygon area coverage (polygon areas) as multiple possibly intersecting satellite strips forming parts of a same task polygon area of interest and naturally requiring various probes, are distributed over disjoint unrelated cluster nodes. This condition add subtle intricacies to correctly and explicitly reconstruct respective objective function contributions at the upper-scale decision level which is better adapted and purposefully designed for spot target tasks. Meeting temporal constraint imposed by clustering and upper-scale scheduling satellite path solutions at the main-scale cluster task scheduling level may also represent a challenge to guarantee solution optimality. The approach remains nonetheless well-suited for problem instances involving spot targets, single target coverage visit, no task precedence constraints, and perfect quality of observation (success certainty).

Recent work essentially focuses on alternate enhanced metaheuristics and refined task preprocessing approaches to further improve solution quality. But, few modeling frameworks and related problem-solving procedures provably providing a suitable bound on solution quality have been proposed [35], [10] in order to objectively measure the value of their methodology. Latest trends seem to show modestly growing interest for agile satellites, mission scenarios with task diversity and multiplicity emphasis, refined single measure of performance (e.g., area coverage) incorporating quality of service (e.g., probability of success), combined or multiple objectives, and larger area coverage requests generalizing traditional target/‘spot’ orders, as well as new task (e.g., search, detection, identification), resource and temporal constraints. However, the


intertwined downlink scheduling problem naturally ordering imaging observation downloads to ground stations for postprocessing analysis, packaging and consumption purposes, largely remains solved separately, despite a certain analogy between the operations research “pick-up and delivery problem with time windows” (a generalization of the VRPTW), and the integrated image acquisition and downloading problem.


5 Application Domains

5.1 Arctic Intelligence

A Canadian priority is to exercise sovereignty, ensure defence, security and safety in the Arctic [52]–[57]. Special attention shall be devoted to the impact of increasing competition over Arctic resources driven by increasing maritime & air traffic, prospect of economic development (natural resources), man-made disaster and environmental concerns.

The pursued objectives are respectively to:

perform surveillance, search-and-rescue and interdiction missions, support domestic agencies for the control in the region;

monitor and secure areas (particular attention: Northwest Passage); and

search and rescue, surveying oil pollution and counterterrorism while securing Arctic sea lines of communication.

In other respect, Canada Joint Task Force North (JTFN) priority [54] consists to improve northern surveillance capability through the use of space-based sensors, high altitude, long endurance unmanned aerial vehicles, high frequency surface wave radar, the emergence of a joint maritime intelligence system based upon networked surface surveillance and undersea detection, as well as intelligence and monitoring capability sharing. Intelligence and monitoring capability sharing could even be further extended at an international level. This would be consistent with the United States Department of Defense 2013 Arctic Strategy [58], the United States Navy Arctic Roadmap 2014–2030 [59] and recent United States Government Accountability Office report on Arctic planning [60], primarily aimed to “ensure security, support safety, and promote defense cooperation, and prepare to respond to a wide range of challenges and contingencies, operating in conjunction with other nations when possible, and independently if necessary in order to maintain stability in the region” as summarily pictured in Figure 6. Accordingly, some eventual CA-US Defence collaborative effort proposal should naturally build or revolve around common interests including:

Limited cooperative resource sharing to improve situational awareness

Common Maritime Picture of the Arctic Ocean

Joint Maritime Domain Awareness (MDA)—information sharing

Distributed resource-sharing for Joint Situational Awareness—task scheduling

Search and rescue, ISR


Figure 6: Arctic—exercise Sovereignty, ensure Defence, Security, Safety—inspired from [59].

Partly reproduced with permission from the Office of the Oceanographer of the Navy. The original can be found in [59].

5.2 CF Projects from the Defence Space Program

Key CF projects from the Defence space program constitute potential application domain as well, namely: Polar Epsilon 2 (PE2), Space Situational Awareness, Bluestone, and Joint Space Support respectively. An excerpt drawn from [61] giving respective project description is summarized as follows:

“Polar Epsilon 2 (PE2) is the follow-on project to Polar Epsilon (Joint Space Based Wide Area Surveillance and Support Capability) [3]. PE2 will enhance capabilities increasing near real-time SA of activities in Canada’s three ocean approaches and through increased surveillance persistence of the Arctic and global areas of interest. Complete wide area coverage up to four times daily of Canadian Arctic waters or land will be provided by low resolution imagery.

Sapphire Space Surveillance – Space Situational Awareness (SSA): The SAPPHIRE system will provide tracking data on Earth-orbiting space objects. SAPPHIRE will be a single satellite-based EO sensor system, including data processing and control ground segments. It will include all those functions required to perform surveillance of space objects upon receipt of surveillance taskings from the CF Operations Centre and deliver space object surveillance data back to the CF Operations.


BLUESTONE is the follow-on project to the Surveillance of Space Project (SAPPHIRE). BLUESTONE will provide surveillance of space data through space-based and/or ground-based sensors. SSA is a critical element to the safety of space flight, of understanding adversaries’ capabilities, to being able to protect Canadian sovereign interests and to avert future collisions in space.

The Joint Space Support Project will enhance the ability of commanders and their staff to plan and conduct CF operations at the tactical and operational levels by downloading imagery directly from commercial satellites as they pass over areas of interest. The high-resolution imagery will be unclassified, timely, and exploited directly in theatre to support Task Force missions. For domestic operations, these images could be distributed to disaster relief, security, or OGDs requiring access to timely information derived from space.”

5.3 DRDC Initiatives

Alternate relevant DRDC project initiatives anticipated to explicitly benefit from the development of an intelligence collection capability include Joint Intelligence Collection and Analysis Capability (JICAC) supporting Joint Forces and the Army, Maritime Domain Awareness (SBMDA) and Space-based Radar Exploitation (SBRE) targeting Joint Forces, as well as Army Land Tactical C4ISR (total asset visibility) for sensor-mission assignment to enhance collection in distributed time-constrained uncertain environment involving a constrained mobile ad-hoc sensor network as reported in [62]. A brief description of these initiatives is reported in [1]. Another applicable upcoming DRDC initiative includes All Domain Situational Awareness (ADSA) [63].


6 Conclusion

An intelligence collection tasking problem statement within the context of the JICAC project has been reported. Contextualizing intelligence collection, typical collection environment properties have been defined while mapping the resource allocation problem to a high-level resource planning and scheduling perspective. Accordingly, a multi-satellite intelligence collection scheduling problem instance statement targeted to a space-based ISR application domain has been depicted describing feature characteristics and attributes of key elements such as intelligence requests and tasks, collection assets, constraints and pursued objectives.

Future work envisions to further progress concept development proposing suitable modeling framework and solution design, concept implementation and validation (analysis) following a phased approach. Accordingly, the next step will naturally consist to increasingly develop or adapt suitable decision model suites and design collection planning and scheduling problem-solving concepts and algorithms, implement them, and conduct performance comparison in order to assess the value of near optimal intelligence collection against known policy, benchmarking strategies or alternate proposed methods. Follow-on anticipated work will further expand problem models and solving methods to handle growing planning complexity, and generalize the developed modeling and problem-solving approaches to possibly make it application domain independent and exportable to promote full or partial reutilization to alternate application domains. Other longer term directions consists in further closing the gap between sensemaking and collection tasking (resource allocation), and to integrate the emerging capability within an evolutionary “Collection Manager Assistant” research prototype anticipated to work in tandem with a feeding collection management awareness component to be developed separately, in order to support real collection managers and analysts.


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References

[1] Berger, J. (2016). Intelligence Collection Investigation. DRDC-RDDC-2016-RXXX, (in process).

[2] Spitzer, M., Kappes, E., Böker, D. (2012). Enhanced intelligence through optimized TCPED concepts for airborne ISR, Proceedings of the SPIE, Volume 8360, id. 83600A.

[3] Butler, P.J. (2005). Project Polar Epsilon: Joint Space-Based Wide Area Surveillance And Support Capability, Proceedings of the 2005 IEEE International Geoscience and Remote Sensing, Symposium (IGARSS), Vol. 2, p. 1194.

[4] Sun-synchronous orbit. https://en.wikipedia.org/wiki/Sun-synchronous_orbit (Access date: Jan 2016).

[5] Headquarters, Department of the Army (2013). FM 3-55 Information Collection.

[6] Mitrovic Minic, S. (2016). Collection Planning Management: Multi-Satellite Collection Scheduling – Task 1 Report: Problem Statement and Literature Review Contract No. W7701-125337/001/QCL, DRDC-RDDC-2016-C228.

[7] Verfaillie, G. (2013). Planning and Scheduling Activities for Earth Surveillance and Observation Satellites: A Constraint-based Perspective, ICAPS 2013 Summer School, Perugia, Italy.

[8] Verfaillie, G., Lemaitre, M. (2006). Tutorial on Planning Activities for Earth Watching and Observation satellites and Constellations: from Off-line Ground Planning to On-line On-board Planning, ICAPS 2006, The English Lake District, Cumbria, UK http://icaps06.icaps-conference.org/ (Access date: Jan 2016).

[9] Lemaitre, et al. (2002). Selecting and Scheduling observations of agile satellites. Aerospace Science and Technology, pp. 367–381.

[10] Nelson, F. (2012) “Scheduling optimization for imagery satellite constellations using column generation”, PhD Thesis, George Mason University, Department of Systems Engineering and Operations Research, Volgenau School of Engineering.

[11] Wang, J., et al. (2015). Exact and inexact scheduling algorithms for multiple earth observation satellites under uncertainties of clouds, EJOR print submitted to European Journal of Operational Research.

[12] Wang, J., et al. (2014). A pure proactive scheduling algorithm for multiple earth observation satellites under uncertainties of clouds, submitted to Computers & Operations Research.

[13] Galceran, E., Carreras, E. (2013). A survey on coverage path planning for robotics. Robotics and Autonomous Systems.

https://en.wikipedia.org/wiki/Sun-synchronous_orbit

http://icaps06.icaps-conference.org/


[14] Vasquez, M., Hao, J. (2001). A logic-constrained knapsack formulation and a tabu algorithm for the daily photograph scheduling of an earth observation satellite. Comput Optim Appl, 20(2): 137–157.

[15] Vasquez, M., Hao, J. (2003). Upper bounds for the SPOT 5 daily photograph scheduling problem. J Comb Optim, 7(1): 87–103.

[16] Wolfe J., Stephen S.E. (2000). Three scheduling algorithms applied to the earth observing systems domain. Manag Sci, 46(1): 148–168.

[17] Lemaitre, M., Verfaillie, G., Jouhaud, F., Lachiver, J.M., Bataille, N. (2000) How to manage the new generation of agile earth observation satellites. In: Proceeding of the international symposium on artificial intelligence, robotics and automation in space (ISAIRAS’00).

[18] Verfaillie, G., Schiex, T. (1994). Solution reuse in dynamic constraint satisfaction problems. In: Proceeding of the twelfth national conference on artificial intelligence (AAAI’94), 307–312.

[19] Gabrel V., Moulet ,A., Murat C., Paschos V.T. (1997). A new single model and derived algorithms for the satellite shot planning problem using graph theory concepts. Ann Oper Res 69: 115–134.

[20] Gabrel V., Vanderpooten D. (2002). Enumeration and interactive selection of efficient paths in a multiple criteria graph for scheduling an earth observing satellite. Eur J Oper Res 139(3): 533–542.

[21] Sarkheyli, A., Vaghei, B.G., Bagheri, A. (2010). New tabu search heuristic in scheduling earth observation satellites. In: Proceeding of the second international conference on software technology and engineering (ICSTE’10).

[22] Zufferey, N., Amstutz, P., Giaccari, P. (2008). Graph colouring approaches for a satellite range scheduling problem. J Scheduling 11(4): 263–277.

[23] Benoist, T., Rottembourg, B. (2004). Upper bounds for revenue maximization in a satellite scheduling problem. 4OR-Q J Oper Res 2004; 2(3): 235–249.

[24] Habet, D., Vasquez, M. (2004). Solving the selecting and scheduling satellite photographs problem with a consistent neighborhood heuristic. In: Proceeding of the sixteenth IEEE international conference on tools with artificial intelligence (ICTAI’04).

[25] Habet, D. (2009). Tabu search to solve real-life combinatorial optimization problems: A case of study. In: Abraham A, Hassanien AE, Carvalho AP, editors. Foundations of Computational Intelligence, Springer 3: 129–151.

[26] Habet, D., Vasquez, M., Vimont, Y. (2010). Bounding the optimum for the problem of scheduling the photographs of an agile earth observing satellite. Comput Optim Appl, 47(2): 307–333.


[27] Liao, D., Tang, Y. (2005). Satellite imaging order scheduling with stochastic weather condition forecast. In: Proceeding of the IEEE international conference on systems, man and cybernetics, 3: 2524–2529.

[28] Liao, D., Tang, Y. (2007). Imaging order scheduling of an earth observation satellite. IEEE Trans Syst Man Cybern, 37(5): 794–802.

[29] Lin, W., Liu, C., Liao, D., Lee, Y. (2003). Daily imaging scheduling of an earth observation satellite. In: Proceeding of the IEEE international conference on systems, man and cybernetics, 2: 1886–1891.

[30] Lin, W., Liao, D. (2004). A tabu search algorithm for satellite imaging scheduling. In: Proceeding of the IEEE international conference on systems, man and cybernetics, 2: 1601–1606.

[31] Lin, W., Chang, S. (2005). Hybrid algorithms for satellite imaging scheduling. In: Proceeding of the IEEE international conference on systems, man and cybernetics, 3: 2518–2523.

[32] Lin, W., Liao, D., Liu, C., Lee, Y. (2005). Daily Imaging Scheduling of an Earth Observation Satellite. IEEE Trans Syst Man Cybern A, 35(2): 213–223.

[33] Marinelli, F., Salvatore, N., Rossi, F., Smriglio, S. (2011). A lagrangian heuristic for satellite range scheduling with resource constraints. Comput Oper Res, 38(11): 1572–1583.

[34] Bianchessi, N., Righini, G. (2006). A mathematical programming algorithm for planning and scheduling an earth observing SAR constellation. In: Proceeding of the fifth international workshop on planning and scheduling for space (IWPSS’06).

[35] Bianchessi, N., Laporte, G. et al. (2007). A heuristic for the multi-satellite, multi-orbit and multi-user management of earth observation satellites, Eur. J. Oper. Res. 177(2): 750–762.

[36] Gabrel, V., Murat, C. (2003) Mathematical programming for earth observation satellite mission planning. In: Ciriani, T.A., Fasano, G., Gliozzi, S., Tadei, R., editors. Operations research in space and air, Springer, pp.103–122.

[37] Gabrel, V. (2006), Strengthened 0-1 linear formulation for the daily satellite mission planning. J Comb Optim, 11(3): 341–346.

[38] Hall, N.G., (1994). Magazine MJ. Maximizing the value of a space mission. Eur J Oper Res, 78(2): 224–241.

[39] Bensana, E., Verfaillie, G., Agnese, J.C., Bataille, N., Blumestein, D. (1996). Exact and inexact methods for the daily management of an earth observation satellite. In: Proceeding of the international symposium on space mission operations and ground data system, 4: 507–514.

[40] Li, J., Chen, H., Jing, N. (2014). A data transmission scheduling algorithm for rapid-response earth-observing operations. Chinese J Aeronaut; doi: http://dx.doi.org/10.1016/j.cja.

http://dx.doi.org/10.1016/j.cja


[41] Tangpattanakul, P., Jozefowiez, N., Lopez, P. (2012). Multi-objective optimization for selecting and scheduling observations. In: Coello, C.A., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M., editors. Lecture Notes in Computer Science, Springer.

[42] Li, Y., Wang, R., Xu, M. (2014). Rescheduling of observing spacecraft using fuzzy neural network and ant colony algorithm. Chinese J Aeronaut; doi: http://dx.doi.org/10.1016/j.cja.

[43] Wang, H., Xu, M., Wang, R., Li, Y. (2009). Scheduling earth observing satellites with hybrid ant colony optimization algorithm. In: Proceeding of the international conference on artificial intelligence and computational intelligence (AICI’09).

[44] Wu, G., Liu, J., Ma, M., Qiu, D. (2013). A two-phase scheduling method with the consideration of task clustering for earth observing satellites. Comput Oper Res, 40(7): 1884–1894.

[45] Globus, A., Crawford, J., Lohn, J., Morris, R. (2002). Scheduling earth observing fleets using evolutionary algorithms: Problem description and approach. In: Proceeding of the third international NASA workshop on planning and scheduling for space.

[46] Globus, A., Crawford, J., Lohn, J., Pryor, A. (2003). A comparison of techniques for scheduling fleets of earth-observing. J Oper Res Soc, 56(8): 962–968.

[47] Agnese, J.C., Bensana, E. (1995). Exact and approximate methods for the daily management of an earth observation satellite. In: Proceeding of the fifth ESA workshop on artificial intelligence and knowledge based systems for space.

[48] Bensana, E., Verfaillie, G., Agnese, J.C., Bataille, N., Blumestein, D. (1996). Exact and inexact methods for the daily management of an earth observation satellite. In: Proceeding of the international symposium on space mission operations and ground data systems, 4: 507–514.

[49] Bianchessi, N., Piuri, V., Righini, G., Roveri, M., Laneve, G., Zigrino, A. (2004). An optimization approach to the planning of earth observing satellites. In: Proceeding of the fourth international workshop on planning and scheduling for space (IWPSS’04).

[50] Bianchessi, N., Righini, G. (2008). Planning and scheduling algorithms for the COSMOSkyMed constellation. Aerosp Sci Technol, 12(7): 535–544.

[51] Verfaillie, G., Lemaitre, M., Schiex, T. (1996). Russian doll search for solving constraint optimization problems. In: Proceeding of the thirteenth national conference on Artificial intelligence (AAAI’96); pp.181–187.

[52] Canada First Defence Strategy (2008).

[53] Canada’s Northern Strategy: Our North, Our Heritage, Our Future (2009). (http://www.northernstrategy.gc.ca/index-eng.asp) (Access date: Feb 2016).

[54] Vachon, P., Secker, J., Werle, D. (2007). Remote sensing in support of Arctic intelligence – Development of sensor selection rules for CSIAPS, DRDC Ottawa, TM 2007-109.

http://dx.doi.org/10.1016/j.cja

http://www.northernstrategy.gc.ca/index-eng.asp


[55] Rossell, J.A. (2011). Arctic Sovereignty/Security in the North through the Use of Advanced Technologies and Sustained Development of Northern Communities.

[56] Charron, A. et al. (2014) NORAD in Perpetuity? Challenges and Opportunities for Canada, UNCLASSIFIED.

[57] Northern approaches: Army Arctic concept 2021 (2013). http://open.canada.ca/vl/en/doc/publications-449586 (Access date: Feb 2016).

[58] Arctic Strategy (2013). Department of Defense Washington DC.

[59] Chief of Naval Operations, The United States Navy Arctic Roadmap for 2014–2030 (2014). Department of Defense Washington DC.

[60] United States Government Accountability Office 15-566 (2015). ARCTIC PLANNING – DOD Expects to Play a Supporting Role to Other Federal Agencies and Has Efforts Under Way to Address Capability Needs and Update Plans.

[61] Grandy, C. et al. (2012). Recommendations On A Royal Canadian Air Force C4ISR Strategy & Plan 2012–2027.

[62] DRDC (2014), 02CA Land Tactical C4ISR/EW Project Charter, Army S&T Portfolio.

[63] Lavoie, P. (2015). All Domain Situational Awareness (ADSA) Initiative – Addressing Technology Gaps for Monitoring Canadian Territory and Waters, Briefing to STOC.

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List of Symbols/Abbreviations/Acronyms/Initialisms

ADSA All Domain Situational Awareness

AIS Automated Identification System

AOI Area of Interest

CCIRM Collection Coordination and Intelligence Requirements Management

CF Canadian Forces

CM Collection Management

CORA Centre for Operational Research and Analysis

DRDC Defence Research and Development Canada

EEI Essential Elements of Information

EO Electro-Optical

GEOINT Geographical Intelligence

IMINT Imagery Intelligence

IPB Intelligence Preparation of the Battlefield

IR Infra-red

ISR Intelligence, Surveillance and Reconnaissance

JFD Joint Force Development

JICAC Joint Information Collection and Analysis Capability

JTFN Joint Task Force North

MDA Maritime Domain Awareness

MOE Measure of Effectiveness

MOP Measure of Performance

NIIRS National Image Interpretability Rating Scale

OGD Other Government Departments

PE2 Polar Epsilon 2

PIR Prioritized Intelligence Requirements

RA Resource Allocation

RDDC Recherche et Développement Defense Canada

RFI Request for Information

S&T Science and Technology

SAR Synthetic Aperture Radar

http://fas.org/irp/doddir/army/ioac/ipb.htm


SBMDA Space-based Maritime Domain Awareness

SBRE Space-based Radar Exploitation

SM Sense-making

SSA Space Situational Awareness

TCPED Tasking, Collection, Processing, Exploitation, Dissemination

VoI Value of Information

VRPTW Vehicle Routing Problem with Time Windows

DOCUMENT CONTROL DATA (Security markings for the title, abstract and indexing annotation must be entered when the document is Classified or Designated)

1. ORIGINATOR (The name and address of the organization preparing the document. Organizations for whom the document was prepared, e.g., Centre sponsoring a contractor's report, or tasking agency, are entered in Section 8.) DRDC – Valcartier Research Centre Defence Research and Development Canada 2459 route de la Bravoure Quebec (Quebec) G3J 1X5 Canada

2a. SECURITY MARKING (Overall security marking of the document including special supplemental markings if applicable.)

UNCLASSIFIED

2b. CONTROLLED GOODS

(NON-CONTROLLED GOODS) DMC A REVIEW: GCEC DECEMBER 2013

3. TITLE (The complete document title as indicated on the title page. Its classification should be indicated by the appropriate abbreviation (S, C or U) in

parentheses after the title.) Multi-Satellite Intelligence Collection Scheduling : Problem Statement

4. AUTHORS (last name, followed by initials – ranks, titles, etc., not to be used) Berger, J.

5. DATE OF PUBLICATION (Month and year of publication of document.) September 2016

6a. NO. OF PAGES (Total containing information, including Annexes, Appendices, etc.)

42

6b. NO. OF REFS (Total cited in document.)

63 7. DESCRIPTIVE NOTES (The category of the document, e.g., technical report, technical note or memorandum. If appropriate, enter the type of report,

e.g., interim, progress, summary, annual or final. Give the inclusive dates when a specific reporting period is covered.) Scientific Report

8. SPONSORING ACTIVITY (The name of the department project office or laboratory sponsoring the research and development – include address.) DRDC – Valcartier Research Centre Defence Research and Development Canada 2459 route de la Bravoure Quebec (Quebec) G3J 1X5 Canada

9a. PROJECT OR GRANT NO. (If appropriate, the applicable research and development project or grant number under which the document was written. Please specify whether project or grant.)

9b. CONTRACT NO. (If appropriate, the applicable number under which the document was written.)

10a. ORIGINATOR’S DOCUMENT NUMBER (The official document number by which the document is identified by the originating activity. This number must be unique to this document.) DRDC-RDDC-2016-R181

10b. OTHER DOCUMENT NO(s). (Any other numbers which may be assigned this document either by the originator or by the sponsor.) 05da

11. DOCUMENT AVAILABILITY (Any limitations on further dissemination of the document, other than those imposed by security classification.)

Unlimited

12. DOCUMENT ANNOUNCEMENT (Any limitation to the bibliographic announcement of this document. This will normally correspond to the Document Availability (11). However, where further distribution (beyond the audience specified in (11) is possible, a wider announcement audience may be selected.)) Unlimited

13. ABSTRACT (A brief and factual summary of the document. It may also appear elsewhere in the body of the document itself. It is highly desirable that the abstract of classified documents be unclassified. Each paragraph of the abstract shall begin with an indication of the security classification of the information in the paragraph (unless the document itself is unclassified) represented as (S), (C), (R), or (U). It is not necessary to include here abstracts in both official languages unless the text is bilingual.)

Under the DRDC Joint Intelligence Collection and Analysis Capability (JICAC) project, this report formulates an intelligence collection tasking problem statement for suitable Canadian Forces (CF) application intelligence domains. Contextualizing intelligence collection based upon environment properties and a high-level resource planning and scheduling framework, a specific intelligence collection tasking problem is defined. Accordingly, a multi-satellite intelligence collection scheduling problem of interest is stated, describing intelligence tasks, collection assets, related respective characteristics, most salient features, attributes and constraints, along with possible pursued objectives. The document also briefly surveys key relevant work from open literature and, identify relevant CF application domains including space-based intelligence, surveillance and reconnaissance (ISR).

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Sous l’égide du projet de Capacité de Cueillette et d’Analyse de Renseignement Inter–armées ou « Joint Intelligence Collection and Analysis Capability » (JICAC) de RDDC, ce rapport énonce un problème de planification de tâches de cueillette du renseignement pour des domaines d’applications pertinents du renseignement des Forces Canadiennes. Un problème de cueillette du renseignement fondé sur des propriétés environnementales et un cadre de planification et d’ordonnancement des ressources est présenté. Un problème d’intérêt d’ordonnancement multi-satellite de cueillette est ainsi défini, décrivant des tâches de renseignement, des ressources de cueillette, leurs caractéristiques respectives, leurs principaux traits, attributs et contraintes, ainsi que les objectifs à atteindre. De plus, le document brièvement dresse un sommaire succinct des contributions de la littérature scientifique puis identifie des domaines pertinents d’applications des Forces Canadiennes, notamment pour le renseignement, la surveillance et reconnaissance effectués à partir de l’espace.

14. KEYWORDS, DESCRIPTORS or IDENTIFIERS (Technically meaningful terms or short phrases that characterize a document and could be helpful in cataloguing the document. They should be selected so that no security classification is required. Identifiers, such as equipment model designation, trade name, military project code name, geographic location may also be included. If possible keywords should be selected from a published thesaurus, e.g., Thesaurus of Engineering and Scientific Terms (TEST) and that thesaurus identified. If it is not possible to select indexing terms which are Unclassified, the classification of each should be indicated as with the title.) intelligence; collection tasking; multi-satellite; scheduling; space-based intelligence; surveillance and reconnaissance

Multi-Satellite Intelligence Collection Scheduling · Résumé . Sous l’égide du projet de...

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