LEADER 05188nam 22006735 450 001 996503549103316 005 20240223144704.0 010 $a3-031-13331-5 024 7 $a10.1007/978-3-031-13331-2 035 $a(MiAaPQ)EBC7165729 035 $a(Au-PeEL)EBL7165729 035 $a(CKB)25913955100041 035 $a(DE-He213)978-3-031-13331-2 035 $a(PPN)267816561 035 $a(EXLCZ)9925913955100041 100 $a20221229d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aCoherence$b[electronic resource] $eIn Signal Processing and Machine Learning /$fby David Ramírez, Ignacio Santamaría, Louis Scharf 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (495 pages) 311 08$aPrint version: Ramírez, David Coherence Cham : Springer International Publishing AG,c2023 9783031133305 320 $aIncludes bibliographical references (pages 467-482) and index. 327 $aIntroduction -- Historical perspective, motivating problems, and preview of what is to come -- Least Squares and related -- Classical correlations and coherence -- Coherence in the multivariate normal (MVN) model -- Classical tests for correlation -- One-channel matched subspace detectors -- Adaptive subspace detectors -- Two channel matched subspace detectors -- Detection of spatially-correlated time series -- Coherence and the detection of cyclostationarity -- Partial coherence for testing causality -- Subspace averaging -- Coherence and performance bounds -- Variations on coherence -- Conclusion. 330 $aThis book organizes principles and methods of signal processing and machine learning into the framework of coherence. The book contains a wealth of classical and modern methods of inference, some reported here for the first time. General results are applied to problems in communications, cognitive radio, passive and active radar and sonar, multi-sensor array processing, spectrum analysis, hyperspectral imaging, subspace clustering, and related. The reader will find new results for model fitting; for dimension reduction in models and ambient spaces; for detection, estimation, and space-time series analysis; for subspace averaging; and for uncertainty quantification. Throughout, the transformation invariances of statistics are clarified, geometries are illuminated, and null distributions are given where tractable. Stochastic representations are emphasized, as these are central to Monte Carlo simulations. The appendices contain a comprehensive account of matrix theory, the SVD, the multivariate normal distribution, and many of the important distributions for coherence statistics. The book begins with a review of classical results in the physical and engineering sciences where coherence plays a fundamental role. Then least squares theory and the theory of minimum mean-squared error estimation are developed, with special attention paid to statistics that may be interpreted as coherence statistics. A chapter on classical hypothesis tests for covariance structure introduces the next three chapters on matched and adaptive subspace detectors. These detectors are derived from likelihood reasoning, but it is their geometries and invariances that qualify them as coherence statistics. A chapter on independence testing in space-time data sets leads to a definition of broadband coherence, and contains novel applications to cognitive radio and the analysis of cyclostationarity. The chapter on subspace averaging reviews basic results and derives an order-fitting rule for determining the dimension of an average subspace. These results are used to enumerate sources of acoustic and electromagnetic radiation and to cluster subspaces into similarity classes. The chapter on performance bounds and uncertainty quantification emphasizes the geometry of the Cramèr-Rao bound and its related information geometry. 606 $aSignal processing 606 $aComputer science$xMathematics 606 $aMathematical statistics 606 $aMachine learning 606 $aSignal, Speech and Image Processing 606 $aProbability and Statistics in Computer Science 606 $aMachine Learning 606 $aProcessament de senyals$2thub 606 $aAprenentatge automàtic$2thub 608 $aLlibres electrònics$2thub 615 0$aSignal processing. 615 0$aComputer science$xMathematics. 615 0$aMathematical statistics. 615 0$aMachine learning. 615 14$aSignal, Speech and Image Processing . 615 24$aProbability and Statistics in Computer Science. 615 24$aMachine Learning. 615 7$aProcessament de senyals 615 7$aAprenentatge automàtic 676 $a006.31 700 $aRamirez$b David$01274066 702 $aSantamari?a$b Ignacio 702 $aScharf$b Louis 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996503549103316 996 $aCoherence$93002282 997 $aUNISA