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E-raamat: Adaptive Detection for Multichannel Signals in Non-Ideal Environments

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  • Formaat: 194 pages
  • Ilmumisaeg: 14-Jun-2024
  • Kirjastus: CRC Press
  • Keel: eng
  • ISBN-13: 9781040030424
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Adaptive Detection for Multichannel Signals in Non-Ideal EnvironmentsSuurem pilt
  • Formaat: 194 pages
  • Ilmumisaeg: 14-Jun-2024
  • Kirjastus: CRC Press
  • Keel: eng
  • ISBN-13: 9781040030424
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"This book systematically presents adaptive multichannel signal detection in three types of non-ideal environments, including sample-starved scenario, signal mismatch scenario, and noise plus subspace interference environment. The authors provide definitions of key concepts, detailed derivations of adaptive multichannel signal detectors, and specific examples for each non-ideal environment. In addition, the possible future trend of adaptive detection methods is discussed, as well as two further research tracks, namely the adaptive detection algorithms based on information geometry and the hybrid approaches that combine adaptive detection algorithms with machine learning algorithms. The book will be of interest to researchers, advanced undergraduate, and graduate students in sonar, radar signal processing, and communications engineering"--

This book systematically presents adaptive multichannel signal detection in three types of non-ideal environments, including sample-starved scenario, signal mismatch scenario, and noise plus subspace interference environment.



This book systematically presents adaptive multichannel signal detection in three types of non-ideal environments, including sample-starved scenario, signal mismatch scenario, and noise plus subspace interference environment.

The authors provide definitions of key concepts, detailed derivations of adaptive multichannel signal detectors, and specific examples for each non-ideal environment. In addition, the possible future trend of adaptive detection methods is discussed, as well as two further research tracks, namely the adaptive detection algorithms based on information geometry and the hybrid approaches that combine adaptive detection algorithms with machine learning algorithms.

The book will be of interest to researchers, advanced undergraduate, and graduate students in sonar, radar signal processing, and communications engineering.

1. Overview for Adaptive Detection
2. Adaptive Detectors in Sample-Starved Environment
3. Adaptive Selective Detectors for Mismatched Signals in the Presence of Signal Mismatch
4. Adaptive Robust Detector in the Presence of Signal Mismatch
5. Tunable Adaptive Detector for Mismatched Signals
6. Adaptive Detection of a Subspace Signal in Interference
7. Future Trends

Zeyu Wang is an associate research fellow with School of Information and Communication Engineering, Beijing University of Posts and Telecommunications. Her research interests include statistical signal processing and radar target detection. She has authored and co-authored more than 20 scientific publications in international journals and conferences.

Weijian Liu is an associate professor at Wuhan Electronic Information Institute, Wuhan, China. His research interests include multichannel signal detection, statistical and array signal processing. He is an editorial board member with several international journals, including IEEE Transactions on Aerospace and Electronic Systems, Signal Processing, and Digital Signal Processing.

Hongmeng Chen (Senior Member, IEEE) is a Senior Engineer with the Beijing Institute of Radio Measurement, Beijing. His research interests include synthetic aperture radar (SAR)/inverse SAR (ISAR) imaging, forward-looking radar (FLR) imaging, ground moving target indication (GMTI), and motion parameter estimation. He has authored and co-authored more than 50 scientific publications in international journals and conferences. He is an editorial board member with several international journals, including Journal of Radars, and Aero Weaponry. He is also an excellent reviewer of the journal Digital Signal Processing.
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