Adaptive Filtering : Algorithms and Practical Implementation / / by Paulo S. R. Diniz

• Record Nr.: UNINA9910366585703321
• Autore: Diniz Paulo S. R
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020
• ISBN: 3-030-29057-3
• Edizione: [5th ed. 2020.]
• Descrizione fisica: 1 online resource (505 pages) : illustrations
• Disciplina: 621.3822
• Soggetti: Signal processing; Image processing; Speech processing systems; Electronic circuits; Electrical engineering; Automatic control; Signal, Image and Speech Processing; Circuits and Systems; Communications Engineering, Networks; Control and Systems Theory
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Includes index.
• Nota di contenuto: Introduction to Adaptive Filtering -- Fundamentals of Adaptive Filtering -- The Least-Mean-Square (LMS) Algorithm -- LMS-Based Algorithms -- LMS-Based Algorithms -- Conventional RLS Adaptive Filter -- Set-Membership Adaptive Filtering -- Adaptive Lattice-Based RLS Algorithms -- Fast Transversal RLS Algorithms -- QR-Decomposition-Based RLS Filters -- Adaptive IIR Filters -- Nonlinear Adaptive Filtering -- Subband Adaptive Filters -- Blind Adaptive Filtering -- Kalman Filtering -- Complex Differentiation -- Quantization Effects in the LMS Algorithm -- Quantization Effects in the RLS Algorithm -- Analysis of Set-Membership Affine Projection Algorithm -- Index.
• Sommario/riassunto: In the fifth edition of this textbook, author Paulo S.R. Diniz presents updated text on the basic concepts of adaptive signal processing and adaptive filtering. He first introduces the main classes of adaptive ; filtering algorithms in a unified framework, using clear notations that facilitate actual implementation. Algorithms are described in tables, which are detailed enough to allow the reader to verify the covered concepts. Examples address up-to-date problems drawn from actual applications. Several chapters are expanded and a new chapter ‘Kalman Filtering’ is included. The book provides a concise background on adaptive filtering, including the family of LMS, affine projection, RLS, set-membership algorithms and Kalman filters, as well as nonlinear, sub-band, blind, IIR adaptive filtering, and more. Problems are included at the end of chapters. A MATLAB package is provided so the reader can solve new problems and test algorithms. The book also offers easy access to working algorithms for practicing engineers.