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Titlebook: Coherence; In Signal Processing David Ramírez,Ignacio Santamaría,Louis Scharf Book 2022 The Editor(s) (if applicable) and The Author(s), un

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书目名称Coherence
副标题In Signal Processing
编辑David Ramírez,Ignacio Santamaría,Louis Scharf
视频video
概述Organizes principles and methods of signal processing and machine learning into the framework of coherence.Applies these principles and methods to model fitting, detection, estimation, space-time seri
图书封面Titlebook: Coherence; In Signal Processing David Ramírez,Ignacio Santamaría,Louis Scharf Book 2022 The Editor(s) (if applicable) and The Author(s), un
描述.This 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 cohe
出版日期Book 2022
关键词Coherence in science and engineering; Coherence in signal processing; Coherence in compressed sensing;
版次1
doihttps://doi.org/10.1007/978-3-031-13331-2
isbn_softcover978-3-031-13333-6
isbn_ebook978-3-031-13331-2
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Coherence, Classical Correlations, and their Invariances,principal angles and with canonical correlations. The study of factorizations of two-channel covariance matrices leads to filtering formulas for MMSE filters and their error covariances. When covariance matrices are estimated from measurements, then the filter and error covariance are random matrice
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Adaptive Subspace Detectors,n noise covariance matrices by constructing covariance estimates from a secondary channel of signal-free measurements. Then the Kelly and Should we say ACE (adaptive coherence estimator) detectors, and their generalizations, are derived as generalized likelihood ratio detectors. These detectors use
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Two-Channel Matched Subspace Detectors,n. We study second-order detectors where the unknown transmitted signal is modeled as a zero-mean Gaussian and averaged out or marginalized and first-order detectors where the unknown transmitted signal appears in the mean of the observations with no prior distribution assigned to it. The signal sub
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Performance Bounds and Uncertainty Quantification,e Cramér-Rao bound for parameters that are carried in the mean value or the covariance matrix of a MVN model. Coherence arises naturally. A concluding section on information geometry ties the Cramér-Rao bound on error covariance to the resolvability of the underlying probability distribution from wh
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