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Integrated Tracking, Classification, and Sensor Management: Theory and Applications [Kõva köide]

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  • Ilmumisaeg: 14-Dec-2012
  • Kirjastus: Wiley-IEEE Press
  • ISBN-10: 0470639059
  • ISBN-13: 9780470639054
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Integrated Tracking, Classification, and Sensor Management: Theory and ApplicationsSuurem pilt
  • Formaat: Hardback, 736 pages, kõrgus x laius x paksus: 243x163x41 mm, kaal: 1143 g, Photos: 50 B&W, 0 Color; Drawings: 20 B&W, 0 Color; Tables: 0 B&W, 0 Color; Graphs: 50 B&W, 0 Color
  • Ilmumisaeg: 14-Dec-2012
  • Kirjastus: Wiley-IEEE Press
  • ISBN-10: 0470639059
  • ISBN-13: 9780470639054
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780470639054.html
Märksõnad:

A unique guide to the state of the art of tracking, classification, and sensor management

This book addresses the tremendous progress made over the last few decades in algorithm development and mathematical analysis for filtering, multi-target multi-sensor tracking, sensor management and control, and target classification. It provides for the first time an integrated treatment of these advanced topics, complete with careful mathematical formulation, clear description of the theory, and real-world applications.

Written by experts in the field, Integrated Tracking, Classification, and Sensor Management provides readers with easy access to key Bayesian modeling and filtering methods, multi-target tracking approaches, target classification procedures, and large scale sensor management problem-solving techniques. Features include:

  • An accessible coverage of random finite set based multi-target filtering algorithms such as the Probability Hypothesis Density filters and multi-Bernoulli filters with focus on problem solving
  • A succinct overview of the track-oriented MHT that comprehensively collates all significant developments in filtering and tracking
  • A state-of-the-art algorithm for hybrid Bayesian network (BN) inference that is efficient and scalable for complex classification models
  • New structural results in stochastic sensor scheduling and algorithms for dynamic sensor scheduling and management
  • Coverage of the posterior Cramer-Rao lower bound (PCRLB) for target tracking and sensor management
  • Insight into cutting-edge military and civilian applications, including intelligence, surveillance, and reconnaissance (ISR)

With its emphasis on the latest research results, Integrated Tracking, Classification, and Sensor Management is an invaluable guide for researchers and practitioners in statistical signal processing, radar systems, operations research, and control theory.

Preface xvii
Contributors xxiii
PART I FILTERING
1 Angle-Only Filtering in Three Dimensions
3(40)
Mahendra Mallick
Mark Morelande
Lyudmila Mihaylova
Sanjeev Arulampalam
Yanjun Yan
1.1 Introduction
3(3)
1.2 Statement of Problem
6(1)
1.3 Tracker and Sensor Coordinate Frames
6(1)
1.4 Coordinate Systems for Target and Ownship States
7(2)
1.4.1 Cartesian Coordinates for State Vector and Relative State Vector
7(1)
1.4.2 Modified Spherical Coordinates for Relative State Vector
8(1)
1.5 Dynamic Models
9(5)
1.5.1 Dynamic Model for State Vector and Relative State Vector in Cartesian Coordinates
9(2)
1.5.2 Dynamic Model for Relative State Vector in Modified Spherical Coordinates
11(3)
1.6 Measurement Models
14(1)
1.6.1 Measurement Model for Relative Cartesian State
14(1)
1.6.2 Measurement Model for Modified Spherical Coordinates
15(1)
1.7 Filter Initialization
15(2)
1.7.1 Initialization of Relative Cartesian Coordinates
16(1)
1.7.2 Initialization of Modified Spherical Coordinates
16(1)
1.8 Extended Kalman Filters
17(2)
1.9 Unscented Kalman Filters
19(4)
1.10 Particle Filters
23(5)
1.11 Numerical Simulations and Results
28(3)
1.12 Conclusions
31(12)
Appendix 1A Derivations for Stochastic Differential Equations in MSC
32(3)
Appendix 1B Transformations Between Relative Cartesian Coordinates and MSC
35(1)
Appendix 1C Filter Initialization for Relative Cartesian Coordinates and MSC
35(5)
References
40(3)
2 Particle Filtering Combined with Interval Methods for Tracking Applications
43(32)
Amadou Gning
Lyudmila Mihaylova
Fahed Abdallah
Branko Ristic
2.1 Introduction
43(1)
2.2 Related Works
44(2)
2.3 Interval Analysis
46(5)
2.3.1 Basic Concepts
46(1)
2.3.2 Inclusion Functions
47(1)
2.3.3 Constraint Satisfaction Problems
48(2)
2.3.4 Contraction Methods
50(1)
2.4 Bayesian Filtering
51(1)
2.5 Box Particle Filtering
52(4)
2.5.1 Main Steps of the Box Particle Filter
52(4)
2.6 Box Particle Filtering Derived from the Bayesian Inference Using a Mixture of Uniform Probability Density Functions
56(9)
2.6.1 Time Update Step
57(6)
2.6.2 Measurement Update Step
63(2)
2.7 Box-PF Illustration over a Target Tracking Example
65(2)
2.7.1 Simulation Set-Up
65(2)
2.8 Application for a Vehicle Dynamic Localization Problem
67(4)
2.9 Conclusions
71(4)
References
72(3)
3 Bayesian Multiple Target Filtering Using Random Finite Sets
75(52)
Ba-Ngu Vo
Ba-Tuong Vo
Daniel Clark
3.1 Introduction
75(1)
3.2 Overview of the Random Finite Set Approach to Multitarget Filtering
76(5)
3.2.1 Single-Target Filtering
76(1)
3.2.2 Random Finite Set and Multitarget Filtering
77(3)
3.2.3 Why Random Finite Set for Multitarget Filtering?
80(1)
3.3 Random Finite Sets
81(4)
3.3.1 Probability Density
82(1)
3.3.2 Janossy Densities
83(1)
3.3.3 Belief Functional and Density
83(1)
3.3.4 The Probability Hypothesis Density
84(1)
3.3.5 Examples of RFS
84(1)
3.4 Multiple Target Filtering and Estimation
85(6)
3.4.1 Multitarget Dynamical Model
86(1)
3.4.2 Multitarget Observation Model
87(1)
3.4.3 Multitarget Bayes Recursion
88(1)
3.4.4 Multitarget State Estimation
88(3)
3.5 Multitarget Miss Distances
91(4)
3.5.1 Metrics
91(1)
3.5.2 Hausdorff Metric
92(1)
3.5.3 Optimal Mass Transfer (OMAT) Metric
92(2)
3.5.4 Optimal Subpattern Assignment (OSPA) Metric
94(1)
3.6 The Probability Hypothesis Density (PHD) Filter
95(10)
3.6.1 The PHD Recursion for Linear Gaussian Models
97(3)
3.6.2 Implementation Issues
100(1)
3.6.3 Extension to Nonlinear Gaussian Models
101(4)
3.7 The Cardinalized PHD Filter
105(6)
3.7.1 The CPHD Recursion for Linear Gaussian Models
107(2)
3.7.2 Implementation Issues
109(1)
3.7.3 The CPHD Filter for Fixed Number of Targets
110(1)
3.8 Numerical Examples
111(6)
3.9 MeMBer Filter
117(10)
3.9.1 MeMBer Recursion
117(1)
3.9.2 Multitarget State Estimation
118(1)
3.9.3 Extension to Track Propagation
119(1)
3.9.4 MeMBer Filter for Image Data
119(3)
3.9.5 Implementations
122(1)
References
122(5)
4 The Continuous Time Roots of the Interacting Multiple Model Filter
127(38)
Henk A.P. Blom
4.1 Introduction
127(2)
4.1.1 Background and Notation
128(1)
4.2 Hidden Markov Model Filter
129(7)
4.2.1 Finite-State Markov Process
129(1)
4.2.2 SDEs Having a Markov Chain Solution
130(1)
4.2.3 Filtering a Hidden Markov Model (HMM)
131(2)
4.2.4 Robust Versions of the HMM Filter
133(3)
4.3 System with Markovian Coefficients
136(5)
4.3.1 The Filtering Problem Considered
136(1)
4.3.2 Evolution of the Joint Conditional Density
136(3)
4.3.3 Evolution of the Conditional Density of xt Given θt
139(2)
4.3.4 Special Cases
141(1)
4.4 Markov Jump Linear System
141(8)
4.4.1 The Filtering Problem Considered
141(1)
4.4.2 Pre-IMM Filter Equations
142(2)
4.4.3 Continuous-Time IMM Filter
144(1)
4.4.4 Linear Version of the Pre-IMM Equations
145(3)
4.4.5 Relation Between Bjork's Filter and Continuous-Time IMM
148(1)
4.5 Continuous-Discrete Filtering
149(5)
4.5.1 The Continuous-Discrete Filtering Problem Considered
149(1)
4.5.2 Evolution of the Joint Conditional Density
149(1)
4.5.3 Continuous-Discrete SIR Particle Filtering
150(2)
4.5.4 Markov Jump Linear Case
152(1)
4.5.5 Continuous-Discrete IMM Filter
152(2)
4.6 Concluding Remarks
154(11)
Appendix 4A Differentiation Rule for Discontinuous Semimardngales
155(1)
Appendix 4B Derivation of Differential for Rt(θ)
156(3)
References
159(6)
PART II MULTITARGET MULTISENSOR TRACKING
5 Multitarget Tracking Using Multiple Hypothesis Tracking
165(38)
Mahendra Mallick
Stefano Coraluppi
Craig Carthel
5.1 Introduction
165(1)
5.2 Tracking Algorithms
166(4)
5.2.1 Tracking with Target Identity (or Track Label)
168(1)
5.2.2 Tracking without Target Identity (or Track Label)
169(1)
5.3 Track Filtering
170(9)
5.3.1 Dynamic Models
171(1)
5.3.2 Measurement Models
172(1)
5.3.3 Single Model Filter for a Nonmaneuvering Target
172(3)
5.3.4 Filtering Algorithms
175(3)
5.3.5 Multiple Switching Model Filter for a Maneuvering Target
178(1)
5.4 MHT Algorithms
179(1)
5.5 Hybrid-State Derivations of MHT Equations
180(5)
5.6 The Target-Death Problem
185(1)
5.7 Examples for MHT
186(3)
5.7.1 Example 1: N-Scan Pruning in Track-Oriented MHT
186(1)
5.7.2 Example 2: Maneuvering Target in Heavy Clutter
187(2)
5.8 Summary
189(14)
References
190(13)
6 Tracking and Data Fusion for Ground Surveillance
203(52)
Michael Mertens
Michael Feldmann
Martin Ulmke
Wolfgang Koch
6.1 Introduction to Ground Surveillance
203(1)
6.2 GMTI Sensor Model
204(5)
6.2.1 Model of the GMTI Clutter Notch
204(2)
6.2.2 Signal Strength Measurements
206(3)
6.3 Bayesian Approach to Ground Moving Target Tracking
209(13)
6.3.1 Bayesian Tracking Filter
210(2)
6.3.2 Essentials of GMTI Tracking
212(2)
6.3.3 Filter Update with Clutter Notch
214(3)
6.3.4 Target Strength Estimation
217(5)
6.4 Exploitation of Road Network Data
222(12)
6.4.1 Modeling of Road Networks
223(2)
6.4.2 Densities on Roads
225(4)
6.4.3 Application: Precision Targeting
229(1)
6.4.4 Track-Based Road-Map Extraction
229(5)
6.5 Convoy Track Maintenance Using Random Matrices
234(9)
6.5.1 Object Extent Within the Bayesian Framework
235(2)
6.5.2 Road-Map Assisted Convoy Track Maintenance
237(5)
6.5.3 Selected Numerical Examples
242(1)
6.6 Convoy Tracking with the Cardinalized Probability Hypothesis Density Filter
243(12)
6.6.1 Gaussian Mixture CPHD Algorithm
244(4)
6.6.2 Integration of Digital Road Maps
248(1)
6.6.3 Target State Dependent Detection Probability
249(1)
6.6.4 Exemplary Results for Small Convoys
250(1)
References
251(4)
7 Performance Bounds for Target Tracking: Computationally Efficient Formulations and Associated Applications
255(56)
Marcel Hernandez
7.1 Introduction
255(3)
7.2 Bayesian Performance Bounds
258(4)
7.2.1 The Estimation Problem
258(1)
7.2.2 A General Class of Lower Bounds
258(2)
7.2.3 Efficient Fixed Dimensionality Recursions
260(2)
7.3 PCRLB Formulations in Cluttered Environments
262(7)
7.3.1 Measurement Model
262(1)
7.3.2 Information Reduction Factor Approach
263(1)
7.3.3 Measurement Sequence Conditioning Approach
264(1)
7.3.4 Measurement Existence Sequence Conditioning Approach
265(1)
7.3.5 Calculation of the Information Reduction Factors
266(2)
7.3.6 Relationships Between the Various Performance Bounds
268(1)
7.4 An Approximate PCRLB for Maneuevring Target Tracking
269(2)
7.4.1 Motion Model
269(1)
7.4.2 Best-Fitting Gaussian Approach
269(1)
7.4.3 Recursive Computation of Best-Fitting Gaussian Approximation
270(1)
7.5 A General Framework for the Deployment of Stationary Sensors
271(23)
7.5.1 Introduction
271(2)
7.5.2 Interval Between Deployments
273(3)
7.5.3 Use of Existing Sensors
276(1)
7.5.4 Locations and Number of New Sensors
277(3)
7.5.5 Performance Measure
280(1)
7.5.6 Efficient Search Technique
281(1)
7.5.7 Example---Sonobuoy Deployment in Submarine Tracking
282(12)
7.6 UAV Trajectory Planning
294(11)
7.6.1 Scenario Overview
294(1)
7.6.2 Measure of Performance
294(1)
7.6.3 One-Step-Ahead Planning
295(1)
7.6.4 Two-Step-Ahead Planning
295(1)
7.6.5 Adaptive Horizon Planning
296(2)
7.6.6 Simulations
298(7)
7.7 Summary and Conclusions
305(6)
References
307(4)
8 Track-Before-Detect Techniques
311(52)
Samuel J. Davey
Mark G. Rutten
Neil J. Gordon
8.1 Introduction
311(7)
8.1.1 Historical Review of TBD Approaches
312(3)
8.1.2 Limitations of Conventional Detect-then-Track
315(3)
8.2 Models
318(9)
8.2.1 Target Model
318(3)
8.2.2 Sensor Model
321(6)
8.3 Baum Welch Algorithm
327(4)
8.3.1 Detection
328(1)
8.3.2 Parameter Selection
329(1)
8.3.3 Complexity Analysis
329(2)
8.3.4 Summary
331(1)
8.4 Dynamic Programming: Viterbi Algorithm
331(3)
8.4.1 Parameter Selection
333(1)
8.4.2 Complexity Analysis
333(1)
8.4.3 Summary
333(1)
8.5 Particle Filter
334(3)
8.5.1 Parameter Selection
336(1)
8.5.2 Complexity Analysis
336(1)
8.5.3 Summary
337(1)
8.6 ML-PDA
337(4)
8.6.1 Optimization Methods
340(1)
8.6.2 Validation
340(1)
8.6.3 Summary
341(1)
8.7 H-PMHT
341(6)
8.7.1 Efficient Two-Dimensional Implementation
344(1)
8.7.2 Nonlinear Gaussian Measurement Function
345(1)
8.7.3 Track Management
346(1)
8.7.4 Summary
346(1)
8.8 Performance Analysis
347(7)
8.8.1 Simulation Scenario
348(1)
8.8.2 Measures of Performance
349(1)
8.8.3 Overall ROC
350(1)
8.8.4 Per-Frame ROC
350(3)
8.8.5 Estimation Accuracy
353(1)
8.8.6 Computation Requirements
353(1)
8.9 Applications: Radar and IRST Fusion
354(3)
8.10 Future Directions
357(6)
References
358(5)
9 Advances in Data Fusion Architectures
363(24)
Stefano Coraluppi
Craig Carthel
9.1 Introduction
363(1)
9.2 Dense-Target Scenarios
364(4)
9.3 Multiscale Sensor Scenarios
368(2)
9.4 Tracking in Large Sensor Networks
370(2)
9.5 Multiscale Objects
372(6)
9.6 Measurement Aggregation
378(5)
9.7 Conclusions
383(4)
References
383(4)
10 Intent Inference and Detection of Anomalous Trajectories: A Metalevel Tracking Approach
387(30)
Vikram Krishnamurthy
10.1 Introduction
387(6)
10.1.1 Examples of Metalevel Tracking
388(2)
10.1.2 SCFGs and Reciprocal Markov Chains
390(1)
10.1.3 Literature Survey
391(1)
10.1.4 Main Results
392(1)
10.2 Anomalous Trajectory Classification Framework
393(2)
10.2.1 Trajectory Classification in Radar Tracking
393(1)
10.2.2 Radar Tracking System Overview
394(1)
10.3 Trajectory Modeling and Inference Using Stochastic Context-Free Grammars
395(8)
10.3.1 Review of Stochastic Context-Free Grammars
396(1)
10.3.2 SCFG Models for Anomalous Trajectories
396(4)
10.3.3 Bayesian Signal Processing of SCFG Models
400(3)
10.4 Trajectory Modeling and Inference Using Reciprocal Processes (RP)
403(3)
10.5 Example 1: Metalevel Tracking for GMTI Radar
406(1)
10.6 Example 2: Data Fusion in a Multicamera Network
407(6)
10.7 Conclusion
413(4)
References
413(4)
PART III SENSOR MANAGEMENT AND CONTROL
11 Radar Resource Management for Target Tracking---A Stochastic Control Approach
417(30)
Vikram Krishnamurthy
11.1 Introduction
417(5)
11.1.1 Approaches to Radar Resource Management
419(1)
11.1.2 Architecture of Radar Resource Manager
420(1)
11.1.3 Organization of
Chapter
421(1)
11.2 Problem Formulation
422(9)
11.2.1 Macro and Micromanager Architecture
422(1)
11.2.2 Target and Measurement Model
423(1)
11.2.3 Micromanagement to Maximize Mutual Information of Targets
424(2)
11.2.4 Formulation of Micromanagement as a Multivariate POMDP
426(5)
11.3 Structural Results and Lattice Programming for Micromanagement
431(6)
11.3.1 Monotone Policies for Micromanagement with Mutual Information Stopping Cost
432(1)
11.3.2 Monotone POMDP Policies for Micromanagement
433(3)
11.3.3 Radar Macromanagement
436(1)
11.4 Radar Scheduling for Maneuvering Targets Modeled as Jump Markov Linear System
437(7)
11.4.1 Formulation of Jump Markov Linear System Model
437(3)
11.4.2 Suboptimal Radar Scheduling Algorithms
440(4)
11.5 Summary
444(3)
References
444(3)
12 Sensor Management for Large-Scale Multisensor-Multitarget Tracking
447(76)
Ratnasingham Tharmarasa
Thia Kirubarajan
12.1 Introduction
447(4)
12.1.1 Sensor Management
447(1)
12.1.2 Centralized Tracking
448(1)
12.1.3 Distributed Tracking
449(1)
12.1.4 Decentralized Tracking
450(1)
12.1.5 Organization of the
Chapter
451(1)
12.2 Target Tracking Architectures
451(1)
12.2.1 Centralized Tracking
451(1)
12.2.2 Distributed Tracking
452(1)
12.2.3 Decentralized Tracking
452(1)
12.3 Posterior Cramer-Rao Lower Bound
452(6)
12.3.1 Multitarget PCRLB for Centralized Tracking
453(5)
12.4 Sensor Array Management for Centralized Tracking
458(15)
12.4.1 Problem Description
458(1)
12.4.2 Problem Formulation
458(7)
12.4.3 Solution Technique
465(1)
12.4.4 Simulation
465(2)
12.4.5 Simulation Results
467(6)
12.5 Sensor Array Management for Distributed Tracking
473(16)
12.5.1 Track Fusion
474(1)
12.5.2 Performance of Distributed Tracking with Full Feedback at Every Measurement Step
475(1)
12.5.3 PCRLB for Distributed Tracking
476(1)
12.5.4 Problem Description
476(1)
12.5.5 Problem Formulation
477(2)
12.5.6 Solution Technique
479(6)
12.5.7 Simulation Results
485(4)
12.6 Sensor Array Management for Decentralized Tracking
489(18)
12.6.1 PCRLB for Decentralized Tracking
490(1)
12.6.2 Problem Description
490(1)
12.6.3 Problem Formulation
491(9)
12.6.4 Solution Technique
500(1)
12.6.5 Simulation Results
501(6)
12.7 Conclusions
507(16)
Appendix 12A Local Search
510(2)
Appendix 12B Genetic Algorithm
512(2)
Appendix 12C Ant Colony Optimization
514(2)
References
516(7)
PART IV ESTIMATION AND CLASSIFICATION
13 Efficient Inference in General Hybrid Bayesian Networks for Classification
523(24)
Wei Sun
Kuo-Chu Chang
13.1 Introduction
523(3)
13.2 Message Passing: Representation and Propagation
526(6)
13.2.1 Unscented Transformation
528(2)
13.2.2 Unscented Message Passing
530(2)
13.3 Network Partition and Message Integration for Hybrid Model
532(4)
13.3.1 Message Integration for Hybrid Model
533(3)
13.4 Hybrid Message Passing Algorithm for Classification
536(1)
13.5 Numerical Experiments
537(7)
13.5.1 Experiment Method
537(3)
13.5.2 Experiment Results
540(2)
13.5.3 Complexity of HMP-BN
542(2)
13.6 Concluding Remarks
544(3)
References
544(3)
14 Evaluating Multisensor Classification Performance with Bayesian Networks
547(32)
Eswar Sivaraman
Kuo-Chu Chang
14.1 Introduction
547(1)
14.2 Single-Sensor Model
548(12)
14.2.1 A New Approach for Quantifying Classification Performance
548(2)
14.2.2 Efficient Estimation of the Global Classification Matrix
550(4)
14.2.3 The Global Classification Matrix: Some Experiments
554(3)
14.2.4 Sensor Design Quality Metrics
557(3)
14.3 Multisensor Fusion Systems---Design and Performance Evaluation
560(4)
14.3.1 Performance Evaluation of Multisensor Models---Good Sensors
560(3)
14.3.2 Performance Evaluation of Multisensor Fusion Systems---Not-so-Good Sensors
563(1)
14.4 Summary and Continuing Questions
564(15)
Appendix 14A Developing a Sensor's Local Confusion Matrix
565(2)
Appendix 14B Solving for the Off-Diagonal Elements of the Global Classification Matrix
567(2)
Appendix 14C A Graph-Theoretic Representation of the Recursive Approach for Estimating the Diagonal Elements of the GCM
569(1)
Appendix 14C.1 The Binomial Case (n = 2, m = 2)
569(2)
Appendix 14C.2 The Multinomial Case (n, m > 2)
571(2)
Appendix 14D Designing Monte Carlo Simulations of the GCM
573(1)
Appendix 14D.1 Single-Sensor GCM
573(1)
Appendix 14D.2 Multisensor GCM
574(1)
Appendix 14E Proof of Approximation 1
574(2)
References
576(3)
15 Detection and Estimation of Radiological Sources
579(40)
Mark Morelande
Branko Ristic
15.1 Introduction
579(1)
15.2 Estimation of Point Sources
580(10)
15.2.1 Model
581(1)
15.2.2 Source Parameter Estimation
581(4)
15.2.3 Simulation Results
585(2)
15.2.4 Experimental Results
587(3)
15.3 Estimation of Distributed Sources
590(9)
15.3.1 Model
591(2)
15.3.2 Estimation
593(2)
15.3.3 Simulation Results
595(3)
15.3.4 Experimental Results
598(1)
15.4 Searching for Point Sources
599(13)
15.4.1 Model
600(1)
15.4.2 Sequential Search Using a POMDP
601(2)
15.4.3 Implementation of the POMDP
603(5)
15.4.4 Simulation Results
608(3)
15.4.5 Experimental Results
611(1)
15.5 Conclusions
612(7)
References
614(5)
PART V DECISION FUSION AND DECISION SUPPORT
16 Distributed Detection and Decision Fusion with Applications to Wireless Sensor Networks
619(42)
Qi Cheng
Ruixin Niu
Ashok Sundaresan
Pramod K. Varshney
16.1 Introduction
619(1)
16.2 Elements of Detection Theory
620(4)
16.3 Distributed Detection with Multiple Sensors
624(10)
16.3.1 Topology
624(2)
16.3.2 Conditional Independence Assumption
626(6)
16.3.3 Dependent Observations
632(2)
16.3.4 Discussion
634(1)
16.4 Distributed Detection in Wireless Sensor Networks
634(11)
16.4.1 Counting Rule in a Wireless Sensor Network with Signal Decay
636(1)
16.4.2 Performance Analysis: Sensors with Identical Statistics
636(1)
16.4.3 Performance Analysis: Sensors with Nonidentical Statistics
637(8)
16.5 Copula-Based Fusion of Correlated Decisions
645(7)
16.5.1 Copula Theory
645(1)
16.5.2 System Design Using Copulas
646(2)
16.5.3 Illustrative Example: Application to Radiation Detection
648(2)
16.5.4 Remark
650(2)
16.6 Conclusion
652(9)
Appendix 16A Performance Analysis of a Network with Nonidentical Sensors via Approximations
653(1)
Appendix 16A.1 Binomial I Approximation
653(1)
Appendix 16A.2 Binomial II Approximation
654(1)
Appendix 16A.3 DeMoivre-Laplace Approximation
654(1)
Appendix 16A.4 Total Variation Distance
655(1)
References
656(5)
17 Evidential Networks for Decision Support in Surveillance Systems
661(44)
Alessio Benavoli
Branko Ristic
17.1 Introduction
661(1)
17.2 Valuation Algebras
662(6)
17.2.1 Mathematical Definitions and Results
664(1)
17.2.2 Axioms
665(2)
17.2.3 Probability Mass Functions as a Valuation Algebra
667(1)
17.3 Local Computation in a VA
668(4)
17.3.1 Fusion Algorithm
668(2)
17.3.2 Construction of a Binary Join Tree
670(2)
17.3.3 Inward Propagation
672(1)
17.4 Theory of Evidence as a Valuation Algebra
672(13)
17.4.1 Combination
676(1)
17.4.2 Marginalization
677(1)
17.4.3 Inferring and Eliciting the Evidential Model
678(3)
17.4.4 Decision Making
681(4)
17.5 Examples of Decision Support Systems
685(20)
17.5.1 Target Identification
685(5)
17.5.2 Threat Assessment
690(9)
Appendix 17A Construction of a BJT
699(1)
Appendix 17B Inward Propagation
700(2)
References
702(3)
Index 705
MAHENDRA MALLICK, PhD, is Principal Research Scientist at the Propagation Research Associates, Inc. A senior member of the IEEE, he has served as the associate editor-in-chief of the online journal of the International Society of Information Fusion (ISIF).

VIKRAM KRISHNAMURTHY, PhD, holds the Canada Research Chair in Statistical Signal Processing at The University of British Columbia. He is an IEEE Fellow and Editor-in-Chief of the IEEE Journal of Selected Topics in Signal Processing.

BA-NGU VO, PhD, is Professor and Chair of Signals and Systems in the Department of Electrical and Computer Engineering at Curtin University in Western Australia. He is Associate Editor for IEEE Transactions on Aerospace and Electronic Systems.