Study on Signal Detection and Recovery Methods with Joint Sparsity (Springer Theses) - Hardcover

Wang, Xueqian

 
9789819941162: Study on Signal Detection and Recovery Methods with Joint Sparsity (Springer Theses)
Hardcover
ISBN 10: 9819941164 ISBN 13: 9789819941162
Verlag: Springer, 2023

Inhaltsangabe

The task of signal detection is deciding whether signals of interest exist by using their observed data. Furthermore, signals are reconstructed or their key parameters are estimated from the observations in the task of signal recovery. Sparsity is a natural characteristic of most of signals in practice. The fact that multiple sparse signals share the common locations of dominant coefficients is called by joint sparsity. In the context of signal processing, joint sparsity model results in higher performance of signal detection and recovery. This book focuses on the task of detecting and reconstructing signals with joint sparsity. The main contents include key methods for detection of joint sparse signals and their corresponding theoretical performance analysis, and methods for joint sparse signal recovery and their application in the context of radar imaging.

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Über die Autorin bzw. den Autor

Dr. Xueqian Wang obtained his Ph.D. degree at Tsinghua University, Beijing, China in 2020. His research is focused on target detection, information fusion, radar imaging, compressed sensing and distributed signal processing. He has published 18 articles in these fields, including 8 IEEE Transactions. Dr. Xueqian Wang has been awarded Postdoctoral Innovative Talent Support Program, Innovative Achievement of Postdoctoral Innovative Talent Support Program, Beijing Outstanding Graduate, and Excellent Doctoral Thesis of Tsinghua University.

Von der hinteren Coverseite

The task of signal detection is deciding whether signals of interest exist by using their observed data. Furthermore, signals are reconstructed or their key parameters are estimated from the observations in the task of signal recovery. Sparsity is a natural characteristic of most of signals in practice. The fact that multiple sparse signals share the common locations of dominant coefficients is called by joint sparsity. In the context of signal processing, joint sparsity model results in higher performance of signal detection and recovery. This book focuses on the task of detecting and reconstructing signals with joint sparsity. The main contents include key methods for detection of joint sparse signals and their corresponding theoretical performance analysis, and methods for joint sparse signal recovery and their application in the context of radar imaging.

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Verlag
Springer
Erscheinungsdatum
2023
Sprache
Englisch
ISBN 10 
9819941164
ISBN 13 
9789819941162
Einband
Gebundene Ausgabe
Anzahl der Seiten
137
Kontakt zum Hersteller
ProductSafety@springernature.com
ProductSafety@springernature.com

Poststr. 9
Darmstadt
64293
Deutschland

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Softcover