03114nam a22005175i 4500 978-981-99-4117-9 DE-He213 20251030110820.0 cr nn 008mamaa 230930s2024 si | s |||| 0|eng d 9789819941179 978-981-99-4117-9 10.1007/978-981-99-4117-9 doi TK5102.9 TJF bicssc UYS bicssc TEC067000 bisacsh TJF thema UYS thema Wang, Xueqian. author. aut http://id.loc.gov/vocabulary/relators/aut 66369 Study on Signal Detection and Recovery Methods with Joint Sparsity [electronic resource] / by Xueqian Wang. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XVI, 121 p. 52 illus., 36 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Springer Theses, Recognizing Outstanding Ph.D. Research, 2190-5061 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. Signal processing. Signal, Speech and Image Processing. SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819941162 Printed edition: 9789819941186 Printed edition: 9789819941193 Springer Theses, Recognizing Outstanding Ph.D. Research, 2190-5061 66370 https://doi.org/10.1007/978-981-99-4117-9 ZDB-2-ENG ZDB-2-SXE 56355 56355