Study on Signal Detection and Recovery Methods with Joint Sparsity

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.

A new detection method is proposed based on the strategy of locally most powerful test with its theoretical analysis It basis-signals with the look-ahead strategy On the two-level block sparsity

Autorentext

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.

Inhalt
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.