Study on Signal Detection and Recovery Methods with Joint Sparsity [electronic resource] / by Xueqian Wang.

Por: Wang, Xueqian [author.]Colaborador(es): SpringerLink (Online service)Tipo de material: TextoTextoSeries Springer Theses, Recognizing Outstanding Ph.D. ResearchEditor: Singapore : Springer Nature Singapore : Imprint: Springer, 2024Edición: 1st ed. 2024Descripción: XVI, 121 p. 52 illus., 36 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9789819941179Tema(s): Signal processing | Signal, Speech and Image ProcessingFormatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 621.382 Clasificación LoC:TK5102.9Recursos en línea: Libro electrónicoTexto
Contenidos:
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. .
En: Springer Nature eBookResumen: 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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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.

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