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Foundations and Applications of Sensor Management

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 f...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Hero, Alfred Olivier (Editor ), Castañón, David (Editor ), Cochran, Doug (Editor ), Kastella, Keith (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York, NY : Springer US : Imprint: Springer, 2008.
Edición:1st ed. 2008.
Colección:Signals and Communication Technology,
Temas:
Acceso en línea:Texto Completo

MARC

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245 1 0 |a Foundations and Applications of Sensor Management  |h [electronic resource] /  |c edited by Alfred Olivier Hero, David Castañón, Doug Cochran, Keith Kastella. 
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490 1 |a Signals and Communication Technology,  |x 1860-4870 
505 0 |a Overview of Book -- Stochastic Control Theory for Sensor Management -- Information Theoretic Approaches to Sensor Management -- Joint Multi-Target Particle Filtering -- Pomdp Approximation Using Simulation and Heuristics -- Multi-Armed Bandit Problems -- Application of Multi-Armed Bandits to Sensor Management -- Active Learning and Sampling -- Plan-In-Advance Active Learning 0f Classifiers -- Application of Sensor Scheduling Concepts to Radar -- Defense Applications -- Appendices. 
520 |a 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. 
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