Note: Supplemental materials are not guaranteed with Rental or Used book purchases.
Purchase Benefits
What is included with this book?
Preface | p. v |
Acknowledgments | p. vii |
Contributing Authors | p. xv |
Symbol Index | p. xvii |
Overview of Book | p. 1 |
Introduction | p. 1 |
Scope of Book | p. 2 |
Book Organization | p. 3 |
Stochastic Control Theory for Sensor Management | p. 7 |
Introduction | p. 7 |
Markov Decision Problems | p. 10 |
Partially Observed Markov Decision Problems | p. 19 |
Approximate Dynamic Programming | p. 26 |
Example | p. 27 |
Conclusion | p. 32 |
Information Theoretic Approaches to Sensor Management | p. 33 |
Introduction | p. 33 |
Background | p. 35 |
Information-Optimal Policy Search | p. 40 |
Information Gain Via Classification Reduction | p. 43 |
A Near Universal Proxy | p. 44 |
Information Theoretic Sensor Management for Multi-target Tracking | p. 47 |
Terrain Classification in Hyperspectral Satellite Imagery | p. 53 |
Conclusion and Perspectives | p. 57 |
Joint Multi-target Particle Filtering | p. 59 |
Introduction | p. 59 |
The Joint Multi-target Probability Density | p. 62 |
Particle Filter Implementation of JMPD | p. 71 |
Multi-target Tracking Experiments | p. 85 |
Conclusions | p. 91 |
POMDP Approximation Using Simulation and Heuristics | p. 95 |
Introduction | p. 95 |
Motivating Example | p. 97 |
Basic Principle: Q-value Approximation | p. 98 |
Control Architecture | p. 101 |
Q-value Approximation Methods | p. 104 |
Simulation Result | p. 116 |
Summary and Discussion | p. 118 |
Multi-armed Bandit Problems | p. 121 |
Introduction | p. 121 |
The Classical Multi-armed Bandit | p. 122 |
Variants of the Multi-armed Bandit Problem | p. 134 |
Example | p. 148 |
Chapter Summary | p. 151 |
Application of Multi-armed Bandits to Sensor Management | p. 153 |
Motivating Application and Overview | p. 153 |
Application to Sensor Management | p. 155 |
Example Application | p. 162 |
Summary and Discussion | p. 173 |
Active Learning and Sampling | p. 177 |
Introduction | p. 177 |
A Simple One-dimensional Problem | p. 179 |
Beyond 1d - Piecewise Constant Function Estimation | p. 190 |
Final Remarks and Open Questions | p. 199 |
Plan-in-Advance Learning | p. 201 |
Introduction | p. 201 |
Analytical Forms of the Classifier | p. 203 |
Pre-labeling Selection of Basis Functions [phi] | p. 204 |
Pre-labeling Selection of Data [chi subscript tr] | p. 209 |
Connection to Theory of Optimal Experiments | p. 210 |
Application to UXO Detection | p. 212 |
Chapter Summary | p. 219 |
Sensor Scheduling in Radar | p. 221 |
Introduction | p. 221 |
Basic Radar | p. 222 |
Measurement in Radar | p. 233 |
Basic Scheduling of Waveforms in Target Tracking | p. 234 |
Measures of Effectiveness for Waveforms | p. 239 |
Scheduling of Beam Steering and Waveforms | p. 245 |
Waveform Libraries | p. 250 |
Conclusion | p. 255 |
Defense Applications | p. 257 |
Introduction | p. 257 |
Background | p. 259 |
The Contemporary Situation | p. 260 |
Dynamic Tactical Targeting (DTT) | p. 262 |
Conclusion | p. 266 |
Appendices | p. 269 |
Information Theory | p. 269 |
Markov Processes | p. 273 |
Stopping Times | p. 278 |
References | p. 283 |
Index | p. 305 |
Table of Contents provided by Ingram. All Rights Reserved. |
The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.
The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.