02927nam a22004695i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001300172072001600185072001600201072002300217072001500240072001500255100008300270245011400353250001800467264007200485300006500557336002600622337002600648338003600674347002400710490007200734505037400806520087501180650002302055650004102078710003402119773002602153776003602179776003602215776003602251830007902287856004602366912001402412912001402426999001702440978-981-99-4117-9DE-He21320251030110820.0cr nn 008mamaa230930s2024    si |    s    |||| 0|eng d  a97898199411799978-981-99-4117-97 a10.1007/978-981-99-4117-92doi 4aTK5102.9 7aTJF2bicssc 7aUYS2bicssc 7aTEC0670002bisacsh 7aTJF2thema 7aUYS2thema1 aWang, Xueqian.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut96636910aStudy on Signal Detection and Recovery Methods with Joint Sparsityh[electronic resource] /cby Xueqian Wang.  a1st ed. 2024. 1aSingapore :bSpringer Nature Singapore :bImprint: Springer,c2024.  aXVI, 121 p. 52 illus., 36 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aSpringer Theses, Recognizing Outstanding Ph.D. Research,x2190-50610 aIntroduction -- 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. .  aThe 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. 0aSignal processing.14aSignal, Speech and Image Processing.2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978981994116208iPrinted edition:z978981994118608iPrinted edition:z9789819941193 0aSpringer Theses, Recognizing Outstanding Ph.D. Research,x2190-506196637040uhttps://doi.org/10.1007/978-981-99-4117-9  aZDB-2-ENG  aZDB-2-SXE  c56355d56355