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Study on signal detection and recovery methods with joint sparsity

Record Type:	Electronic resources : Monograph/item
Title/Author:	Study on signal detection and recovery methods with joint sparsity/ by Xueqian Wang.
Author:	Wang, Xueqian.
Published:	Singapore :Springer Nature Singapore : : 2024.,
Description:	xvi, 121 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Joint Sparse Signal Detection Based On Locally Most Powerful Test Under Gaussian Model -- Joint Sparse Signal Detection Based On Locally Most Powerful Test Under Generalized Gaussian Model -- Joint Sparse Signal Recovery Based On Look-Ahead Selection of Basis-Signals -- Joint Sparse Signal Recovery Based On Two-Level Sparsity -- Summary and Outlook.
Contained By:	Springer Nature eBook
Subject:	Signal detection. -
Online resource:	https://doi.org/10.1007/978-981-99-4117-9
ISBN:	9789819941179

Study on signal detection and recovery methods with joint sparsity
Wang, Xueqian.

Study on signal detection and recovery methods with joint sparsity[electronic resource] /by Xueqian Wang. - Singapore :Springer Nature Singapore :2024. - xvi, 121 p. :ill., digital ;24 cm. - Springer theses,2190-5061. - Springer theses..

Introduction -- Joint Sparse Signal Detection Based On Locally Most Powerful Test Under Gaussian Model -- Joint Sparse Signal Detection Based On Locally Most Powerful Test Under Generalized Gaussian Model -- Joint Sparse Signal Recovery Based On Look-Ahead Selection of Basis-Signals -- Joint Sparse Signal Recovery Based On Two-Level Sparsity -- Summary and Outlook.

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.

ISBN: 9789819941179

Standard No.: 10.1007/978-981-99-4117-9doiSubjects--Topical Terms:

666362
Signal detection.

LC Class. No.: TK5102.9

Dewey Class. No.: 621.3822

Study on signal detection and recovery methods with joint sparsity
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490 1 $a Springer theses, $x 2190-5061
505 0 $a Introduction -- Joint Sparse Signal Detection Based On Locally Most Powerful Test Under Gaussian Model -- Joint Sparse Signal Detection Based On Locally Most Powerful Test Under Generalized Gaussian Model -- Joint Sparse Signal Recovery Based On Look-Ahead Selection of Basis-Signals -- Joint Sparse Signal Recovery Based On Two-Level Sparsity -- Summary and Outlook.
520 $a 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.
650 0 $a Signal detection. $3 666362
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