Buch, Englisch, 310 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 499 g
Buch, Englisch, 310 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 499 g
Reihe: Signals and Communication Technology
ISBN: 978-1-4419-3911-1
Verlag: Springer US
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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
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.
