Ebook: Foundations and Applications of Sensor Management
- Tags: Signal Image and Speech Processing, Control Engineering, Coding and Information Theory, Communications Engineering Networks, Electronic and Computer Engineering
- Year: 2008
- Publisher: Springer US
- Edition: 1
- Language: English
- pdf
Foundations and Applications of Sensor Management presents the emerging theory of sensor management with applications to real-world examples such as landmine detection, adaptive signal and image sampling, multi-target tracking, and radar waveform scheduling. It is written by leading experts in the field for a diverse engineering audience ranging from signal processing, to automatic control, statistics, and machine learning. The level of treatment of the book is tutorial and self-contained.
The chapters of the book follow a logical development from theoretical foundations to approximate approaches and ending with applications. The coverage includes the following topics: stochastic control foundations of sensor management; multi-armed bandits and their connections to sensor management; information-theoretic approaches; managed sensing for multi-target tracking; approximation methods based on embedded simulation; active learning for classification and sampling; and waveform scheduling for radar. An appendix is included to provide essential background on topics the reader may not have encountered as a first-year graduate student: Markov decision processes; information theory; and stopping times.
Foundations and Applications of Sensor Management is an important reference for signal processing and control engineers and researchers as well as machine learning application developers.
Foundations and Applications of Sensor Management presents the emerging theory of sensor management with applications to real-world examples such as landmine detection, adaptive signal and image sampling, multi-target tracking, and radar waveform scheduling. It is written by leading experts in the field for a diverse engineering audience ranging from signal processing, to automatic control, statistics, and machine learning. The level of treatment of the book is tutorial and self-contained.
The chapters of the book follow a logical development from theoretical foundations to approximate approaches and ending with applications. The coverage includes the following topics: stochastic control foundations of sensor management; multi-armed bandits and their connections to sensor management; information-theoretic approaches; managed sensing for multi-target tracking; approximation methods based on embedded simulation; active learning for classification and sampling; and waveform scheduling for radar. An appendix is included to provide essential background on topics the reader may not have encountered as a first-year graduate student: Markov decision processes; information theory; and stopping times.
Foundations and Applications of Sensor Management is an important reference for signal processing and control engineers and researchers as well as machine learning application developers.
Foundations and Applications of Sensor Management presents the emerging theory of sensor management with applications to real-world examples such as landmine detection, adaptive signal and image sampling, multi-target tracking, and radar waveform scheduling. It is written by leading experts in the field for a diverse engineering audience ranging from signal processing, to automatic control, statistics, and machine learning. The level of treatment of the book is tutorial and self-contained.
The chapters of the book follow a logical development from theoretical foundations to approximate approaches and ending with applications. The coverage includes the following topics: stochastic control foundations of sensor management; multi-armed bandits and their connections to sensor management; information-theoretic approaches; managed sensing for multi-target tracking; approximation methods based on embedded simulation; active learning for classification and sampling; and waveform scheduling for radar. An appendix is included to provide essential background on topics the reader may not have encountered as a first-year graduate student: Markov decision processes; information theory; and stopping times.
Foundations and Applications of Sensor Management is an important reference for signal processing and control engineers and researchers as well as machine learning application developers.
Content:
Front Matter....Pages I-XVIII
Overview of Book....Pages 1-5
Stochastic Control Theory for Sensor Management....Pages 7-32
Information Theoretic Approaches to Sensor Management....Pages 33-57
Joint Multi-Target Particle Filtering....Pages 59-93
Pomdp Approximation Using Simulation and Heuristics....Pages 95-119
Multi-Armed Bandit Problems....Pages 121-151
Application of Multi-Armed Bandits to Sensor Management....Pages 153-175
Active Learning and Sampling....Pages 177-200
Plan-In-Advance Active Learning 0f Classifiers....Pages 201-220
Application of Sensor Scheduling Concepts to Radar....Pages 221-256
Defense Applications....Pages 257-268
Appendices....Pages 269-281
Back Matter....Pages 283-308
Foundations and Applications of Sensor Management presents the emerging theory of sensor management with applications to real-world examples such as landmine detection, adaptive signal and image sampling, multi-target tracking, and radar waveform scheduling. It is written by leading experts in the field for a diverse engineering audience ranging from signal processing, to automatic control, statistics, and machine learning. The level of treatment of the book is tutorial and self-contained.
The chapters of the book follow a logical development from theoretical foundations to approximate approaches and ending with applications. The coverage includes the following topics: stochastic control foundations of sensor management; multi-armed bandits and their connections to sensor management; information-theoretic approaches; managed sensing for multi-target tracking; approximation methods based on embedded simulation; active learning for classification and sampling; and waveform scheduling for radar. An appendix is included to provide essential background on topics the reader may not have encountered as a first-year graduate student: Markov decision processes; information theory; and stopping times.
Foundations and Applications of Sensor Management is an important reference for signal processing and control engineers and researchers as well as machine learning application developers.
Content:
Front Matter....Pages I-XVIII
Overview of Book....Pages 1-5
Stochastic Control Theory for Sensor Management....Pages 7-32
Information Theoretic Approaches to Sensor Management....Pages 33-57
Joint Multi-Target Particle Filtering....Pages 59-93
Pomdp Approximation Using Simulation and Heuristics....Pages 95-119
Multi-Armed Bandit Problems....Pages 121-151
Application of Multi-Armed Bandits to Sensor Management....Pages 153-175
Active Learning and Sampling....Pages 177-200
Plan-In-Advance Active Learning 0f Classifiers....Pages 201-220
Application of Sensor Scheduling Concepts to Radar....Pages 221-256
Defense Applications....Pages 257-268
Appendices....Pages 269-281
Back Matter....Pages 283-308
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