
Sensor Management in ISR
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Content
- Sensor Management in ISR
- Contents
- Preface
- 1 Introduction to Sensor Management
- 1.1 Motivation for Sensor Management in Intelligence, Surveillence, and Reconaissance
- 1.2 Sensor Management Versus Data Fusion
- 1.3 Sensor Management Is Motivated by the Need to Inform Situation Assessment
- 1.4 Sensor Management
- 1.5 Sensor Scheduling, Sensor Management, and Mission Management
- 1.6 Optimum Planning Versus Optimum Scheduling
- 1.7 Sensor Suite Viewed as a Constrained Communications Channel
- 1.8 Preliminaries
- 1.9 Road Map for the Sequel
- References
- 2 Historical Basis for Sensor Management
- 2.1 From Task-Specific Sensor to Heterogeneous Network
- 2.2 Integration of Frequency Diverse Radars
- 2.3 Integration of Modality Diverse Sensors During the Vietnam Era
- 2.4 Networks of Homogeneous Sensors
- 2.5 Network of Heterogeneous Sensors
- 2.6 Network-Centric Warfare: The Start of Modern Sensor Management
- References
- 3 Sensor Management Inherent Problems
- 3.1 Indirect Sensor Management Issues
- 3.2 Multidisciplinary Problem
- 3.3 Passive Sensor Issues
- 3.4 Active Sensor Issues
- 3.5 Virtual Sensor, Heterogeneous Sensor, and Pseudo-Sensor Issues
- 3.6 World Models
- 3.6.1 Physical Models
- 3.6.2 Context
- 3.6.3 Probabilistic Models
- 3.6.4 Social Network Models
- 3.7 Operational Issues
- 3.7.1 Myopic Scheduling
- 3.7.2 Sensor Management Objective Functions
- References
- 4 Sensor Management Related Problems
- 4.1 Introduction
- 4.2 Fusion-Related Issues
- 4.2.1 Common Frame of Reference and Merging of Data from Different Platforms
- 4.2.2 Data Association Coordinate System Errors
- 4.2.3 Data Pedigree
- 4.2.4 Data Veracity
- 4.2.5 Hard and Soft Data Fusion
- 4.3 Alternative Configurations for Search, Track, and Identification
- 4.4 Detection Criteria
- 4.5 Target Models
- 4.6 Scheduling Constraints
- 4.6.1 Deleterious Interaction of Sensors
- 4.6.2 Computational Constraints
- 4.6.3 Randomly Occurring Sensor Failures
- References
- 5 Theoretical Approaches to Sensor Management
- 5.1 Overview of Sensor Management Theories
- 5.2 Scheduling Approaches Versus Decision-Making Approaches
- 5.3 Decision Theoretic Approaches
- 5.4 Normative Decision Theoretic Approaches
- 5.5 Descriptive Decision Theoretic Approaches
- 5.6 Sensor Management Architecture-Based Approaches
- 5.6.1 Decentralized Management
- 5.6.2 Game Theory-Based Approaches
- 5.6.3 Market Theory-Based Approaches
- 5.6.4 Hybrid Approaches
- References
- 6 Artificial Intelligence for Sensor Management
- 6.1 Introduction
- 6.2 Resurgence of AI
- 6.3 Specific Mapping of AI Capabilities to IBSM Functions
- 6.4 Supervised Machine Learning
- 6.5 Unsupervised Machine Learning
- 6.6 Data Fusion
- 6.6.1 Implementing Data Fusion Within IBSM
- 6.7 Ontologies for Storage and Reasoning
- 6.8 Characterizing Uncertainty
- 6.9 Qualitative Reasoning
- 6.10 Distributed Cognition for Sensor Management
- 6.11 Levels of Autonomy
- 6.11.1 A Survey of Levels of Autonomy
- 6.11.2 Adaptability Enables Autonomy
- 6.11.3 Measuring Adaptability
- 6.11.4 Foreseeable Adaptation
- 6.11.5 Unforeseeable Adaptation
- 6.11.6 Adaptation Measurement
- 6.12 Measuring the Effectiveness of Autonomy
- 6.13 Control Models for Close Coordination of Sensor Platforms
- 6.14 Machine Learning
- 6.15 Explainable AI
- References
- 7 MQ-4C Triton: a Case Study
- 7.1 Overview of the Triton Broad Area Maritime Surveillance System
- 7.2 A Brief History of Triton
- 7.3 The Triton Sensor Payload
- 7.4 Operational Management of Triton
- 7.5 Doctrinal Guidance for Triton Operation
- 7.6 Sensor Management During a Notional Triton Sortie
- References
- 8 Information Theoretic Approach to Sensor Management
- 8.1 Overview of the IBSM
- 8.2 Data, Information, and Knowledge
- 8.3 Information Measures
- 8.3.1 Fisher Information
- 8.3.2 Kullback- Leibler Divergence (Also Known as Relative Entropy, (InFormation) Divergence)
- 8.3.3 Mutual Information (Also Known as Information Gain)
- 8.3.4 Csiszar-Rényi Generalized Information
- 8.3.5 Entropy
- 8.3.6 Knowledge
- 8.3.7 NIIRS Information
- 8.3.8 IBSM Information Measures
- 8.3.9 Sensor Information
- 8.3.10 Situation Information
- 8.4 Time Value of Information (TVI)
- 8.5 The IBSM Model
- 8.6 Collaboration Among IBSM-Managed Sensor Platforms
- 8.7 Benefits of the IBSM
- 8.8 Summary
- References
- 9 IBSM Optimization Criterion: Expected Information Value Rate
- 9.1 Global, Commensurate, Objective Function
- 9.1.1 EIVRsit Expected Situation Information Value Rate
- 9.1.2 EIVRsen Expected Sensor Information Value Rate
- 9.2 BNCO
- 9.3 Goal Lattice for Situation and Sensor Valuation
- 9.3.1 Goal Lattice Valuation
- 9.3.2 Goal Lattice Computation
- 9.3.3 Method and Apparatus of Measuring a Relative Utility for Each of Several Different Tasks Based on Identified System Goals
- 9.3.4 Goal Lattice Sensitivity
- 9.4 System Goal Lattice Examples
- 9.5 Collaboration Through Goal Lattices
- 9.6 Orchestrated Goal Lattice Engine
- References
- 10 IBSM Implementation Approaches
- 10.1 Introduction
- 10.2 Situation Information Expected Value Network
- 10.2.1 Nonmanaged Nodes
- 10.2.2 Situation Hypothesis Nodes
- 10.2.3 Managed Nodes
- 10.3 Dynamic Bayesian Networks and Situation Information
- 10.4 Sensor Selection and Control Functions
- 10.4.1 AFT
- 10.4.2 Information Instantiator
- 10.4.3 Merging AFT and the Bottom of the Goal Lattice
- 10.4.4 Temporal Constraints
- 10.5 Sensor Scheduler
- 10.6 Communications Manager
- 10.7 Situation Assessment Database (SADB)
- References
- 11 Future Technologies and Implications
- 11.1 Introduction
- 11.2 The IoT as a Sensor System
- 11.3 Cyber-Physical Systems
- 11.4 Fifth Generation (5G) Mobile Communication Networks
- 11.5 Smart Cities
- 11.6 Sensing-as-a-Service Business Models
- 11.7 Social Media as a Sensor
- 11.8 Summary
- References
- Acronyms and Abbreviations
- About the Author
- Index
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