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Foundations and Applications of Sensor Management Signals and Communication Technology

:	Alfred O. Hero, David A. Castañón, Douglas Cochran, Keith Kastella
:	Alfred Olivier Hero, David Castanon, Doug Cochran, Keith Kastella
:	Foundations and Applications of Sensor Management Signals and Communication Technology
:	Springer-Verlag
:	9780387498195
:	1
:	CHF 133.00
:

:	Elektronik, Elektrotechnik, Nachrichtentechnik
:	English

:	310
:	Wasserzeichen
:	PC/MAC/eReader/Tablet
:	PDF

This book covers control theory signal processing and relevant applications in a unified manner. It introduces the area, takes stock of advances, and describes open problems and challenges in order to advance the field. The editors and contributors to this book are pioneers in the area of active sensing and sensor management, and represent the diverse communities that are targeted.

	Preface	6
	Acknowledgments	7
	Contents	8
	Contributing Authors	13
	Symbol Index	15
	OVERVIEW OF BOOK	17
	1. Introduction	17
	2. Scope of Book	18
	3. Book Organization	19
	STOCHASTIC CONTROL THEORY FOR SENSOR MANAGEMENT	22
	1. Introduction	22
	2. Markov Decision Problems	25
	2.1 Dynamic Programming	27
	2.2 Stationary Problems	28
	2.3 Algorithms for MDPs	33
	3. Partially Observed Markov Decision Problems	34
	3.1 MDP Representation of POMDPs	36
	3.2 Dynamic Programming for POMDPs	39
	4. Approximate Dynamic Programming	41
	5. Example	42
	6. Conclusion	47
	INFORMATION THEORETIC APPROACHES TO SENSOR MANAGEMENT	48
	1. Introduction	48
	2. Background	50
	2.1 a-Entropy, a-Conditional Entropy, and a-Divergence	51
	2.2 Relations Between Information Divergence and Risk	53
	2.3 Fisher Information and Information Divergence	55
	3. Information-Optimal Policy Search	55
	4. Information Gain Via Classification Reduction	58
	5. A Near Universal Proxy	59
	6. Information Theoretic Sensor Management for Multi- target Tracking	62
	6.1 The Model Multi-target Tracking Problem	63
	6.2 R ´ enyi Divergence for Sensor Scheduling	64
	6.3 Multi-target Tracking Experiment	65
	6.4 On the Choice of	65
	6.5 Sensitivity to Model Mismatch	66
	6.6 Information Gain vs Entropy Reduction	67
	7. Terrain Classification in Hyperspectral Satellite Imagery	68
	7.1 Optimal Waveform Selection	69
	8. Conclusion and Perspectives	72
	JOINT MULTI-TARGET PARTICLE FILTERING	73
	1. Introduction	73
	2. The Joint Multi-target Probability Density	76
	2.1 General Bayesian Filtering	78
	2.2 Non-Linear Bayesian Filtering for a Single Target	79
	2.3 Accounting for Target Birth and Death	80
	2.4 Computing Renyi Divergence	81
	2.5 Sensor Modeling	82
	3. Particle Filter Implementation of JMPD	85
	3.1 The Single Target Particle Filter	86
	3.2 The Multi-target Particle Filter	87
	3.3 Permutation Symmetry and Improved Importance Densities for JMPD	88
	3.4 Multi-target Particle Proposal Via Individual Target Proposals	89
	3.5 Multi-target Particle Proposal Via Joint Sampling	93
	3.6 Partition Ordering	95
	3.7 Estimation	97
	3.8 Resampling	99
	4. Multi-target Tracking Experiments	99
	4.1 Adaptive Proposal Results	100
	4.2 Partition Swapping	103
	4.3 The Value of Not Thresholding	103
	4.4 Unknown Number of Targets	104
	5. Conclusions	105
	POMDP APPROXIMATION USING SIMULATION AND HEURISTICS	108
	1. Introduction	108
	2. Motivating Example	110
	3. Basic Principle: Q-value Approximation	111
	3.1 Optimal Policy	111
	3.2 Q-values	112
	3.3 Stationary policies	113
	3.4 Receding horizon	113
	3.5 Approximating Q-values	113
	4. Control Architecture	114
	4.1 Controller	115
	4.2 Measurement filter	115
	4.3 Action selector	116
	5. Q-value Approximation Methods	117
	5.1 Basic approach	117
	5.2 Monte Carlo sampling	117
	5.3 Relaxation of optimization problem	118
	5.4 Heuristic approximation	119
	5.5 Parametric approximation	120
	5.6 Action-sequence approximations	122
	5.7 Rollout	123
	5.8 Parallel rollout	124
	5.9 Control architecture in the Monte Carlo case	124
	5.10 Belief-state simplification	127
	5.11 Reward surrogation	128
	6. Simulation Result	129
	7. Summary and Discussion	131
	MULTI-ARMED BANDIT PROBLEMS	133
	1. Introduction	133
	2. The Classical Multi-armed Bandit	134
	2.1 Problem Formulation	135
	2.2 On Forward Induction	137
	2.3 Key Features of the Classical MAB Problem and the Nature of its Solution	139
	2.4 Computational Issues	142
	3. Variants of the Multi-armed Bandit Problem	146
	3.1 Superprocesses	146
	3.2 Arm-acquiring Bandits	149
	3.3 Switching Penalties	150
	3.4 Multiple Plays	152
	3.5 Restless Bandits	154
	3.6 Discussion	159
	4. Example	160
	5. Chapter Summary	163
	APPLICATION OF MULTI-ARMED BANDITS TO SENSOR MANAGEMENT	164
	1. Motivating Application and Overview	164
	1.1 Introduction	164
	1.2 SM Example of Multi-armed Bandit	165
	1.3 Organization and Notation of This Chapter	166
	2. Application to Sensor Management	166
	2.1 Application of the Classical MAB	167
	2.2 Single Sensor with Multiple Modes	170
	2.3 Detecting New Targets	171
	2.4 Sensor Switching Delays	171
	2.5 Multiple Sensors	172
	2.6 Application of Restless Bandits	173
	3. Example Application	173
	3.1 MAB Formulation of SM Tracking Problem	174
	3.2 Index Rule Solution of MAB	175
	3.3 Numerical Results and Comparison to Other Solutions	177
	4. Summary and Discussion	184
	ACTIVE LEARNING AND SAMPLING	187
	1. Introduction	187
	1.1 Some Motivation Examples	188
	2. A Simple One-dimensional Problem	189
	3. Beyond 1d - Piecewise Constant Function Estimation