He was a co-recipient of the 2008 EEEfCOM Innovation Award, and co-author of a paper that received the 2012 IEEE Signal Processing Society Young Author Best Paper Award. Santamaría was general Co-Chair of the 2012 IEEE Workshop on Machine Learning for Signal Processing (MLSP 2012). He served as Associate Editor and Senior Area Editor of the IEEE Transactions on Signal Processing. He has served as a member of the IEEE Machine Learning for Signal Processing and Signal Processing Theory and Methods Technical Committees, and as a steering committee member of the IEEE Data Science Initiative. He has been a visiting researcher at the University of Florida, University of Texas at Austin, and Colorado State University, Fort Collins. He has been involved in numerous national and international research projects on these topics, with more than 200 publications co-authored in refereed journals and international conference proceedings. His research interests lie at the intersection of statistical signal processing, machine learning, and information theory, with special emphasis on applications to wireless communication systems and multi-sensor signal processing for radar and sonar. Ignacio Santamaría is a Professor of Electrical Engineering at the Universidad de Cantabria, Santander, Spain. Ramírez received the 2012 IEEE Signal Processing Society Young Author Best Paper Award and a Certificate of Merit awarded by the IEEE Signal Processing Society “for outstanding editorial board service for the IEEE Transactions on Signal Processing” in 2020. Ramírez has co-authored more than 75 publications in refereed journals and international conferences/workshops. He has participated in many national and international research projects related to the these topics. His research interests are in the area of statistical signal processing, as it applies to wireless communication and bio-medicine. Before joining UC3M, he was Research Associate and Assistant Professor at the Universität Paderborn, Germany, and Visiting Researcher at University of Newcastle, Australia, University College London, and Colorado State University, Fort Collins. The chapter on performance bounds and uncertainty quantification emphasizes the geometry of the Cramèr-Rao bound and its related information geometry.ĭavid Ramírez is Associate Professor of Electrical Engineering at the Universidad Carlos III de Madrid (UC3M). These results are used to enumerate sources of acoustic and electromagnetic radiation and to cluster subspaces into similarity classes. The chapter on subspace averaging reviews basic results and derives an order-fitting rule for determining the dimension of an average subspace. 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. These detectors are derived from likelihood reasoning, but it is their geometries and invariances that qualify them as coherence statistics. A chapter on classical hypothesis tests for covariance structure introduces the next three chapters on matched and adaptive subspace detectors. 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. The book begins with a review of classical results in the physical and engineering sciences where coherence plays a fundamental role. The appendices contain a comprehensive account of matrix theory, the SVD, the multivariate normal distribution, and many of the important distributions for coherence statistics. Stochastic representations are emphasized, as these are central to Monte Carlo simulations. Throughout, the transformation invariances of statistics are clarified, geometries are illuminated, and null distributions are given where tractable. 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. 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 book contains a wealth of classical and modern methods of inference, some reported here for the first time. This book organizes principles and methods of signal processing and machine learning into the framework of coherence.
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