Summary and Info
Leading experts present the latest research results in adaptive signal processingRecent developments in signal processing have made it clear that significant performance gains can be achieved beyond those achievable using standard adaptive filtering approaches. Adaptive Signal Processing presents the next generation of algorithms that will produce these desired results, with an emphasis on important applications and theoretical advancements. This highly unique resource brings together leading authorities in the field writing on the key topics of significance, each at the cutting edge of its own area of specialty. It begins by addressing the problem of optimization in the complex domain, fully developing a framework that enables taking full advantage of the power of complex-valued processing. Then, the challenges of multichannel processing of complex-valued signals are explored. This comprehensive volume goes on to cover Turbo processing, tracking in the subspace domain, nonlinear sequential state estimation, and speech-bandwidth extension.Examines the seven most important topics in adaptive filtering that will define the next-generation adaptive filtering solutionsIntroduces the powerful adaptive signal processing methods developed within the last ten years to account for the characteristics of real-life data: non-Gaussianity, non-circularity, non-stationarity, and non-linearityFeatures self-contained chapters, numerous examples to clarify concepts, and end-of-chapter problems to reinforce understanding of the materialContains contributions from acknowledged leaders in the fieldIncludes a Solutions Manual for instructorsAdaptive Signal Processing is an invaluable tool for graduate students, researchers, and practitioners working in the areas of signal processing, communications, controls, radar, sonar, and biomedical engineering.
More About the Author
Tülay Adalı is a Distinguished University Professor of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County, whose research interests include signal processing, machine learning, and bioinformatics.
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