The field of digital signal processing has developed considerably in the last two decades. This development is related to the growth of available technologies for implementing digital signal processing algorithms. If accurate information on the signals to be processed is available, the designer can easily choose the most appropriate algorithm to process the signal. Fixed algorithms do not process efficiently signals whose statistical properties are unknown. The solution is to use an adaptive filter that automatically changes its characteristics by optimizing its internal parameters. Adaptive filtering algorithms are essential in many statistical signal processing applications.
Adaptive Filtering: Algorithms and Practical Implementation is a concise presentation of adaptive filtering, covering as many algorithms as possible while avoiding adapting notations and derivations related to the different algorithms. Furthermore, the book points out the algorithms which really work in a finite-precision implementation, and provides easy access to the working algorithms for the practicing engineer.
Adaptive Filtering: Algorithms and Practical Implementation may be used as the principal text for courses on the subject, and serves as an excellent reference for professional engineers and researchers in the field.
The author has prepared master transparencies, Matlab routines and an errata file for the book which may be obtained via ftp as follows:
• ftp lps.ufrj.br
• login: anonymous
• password: your email address
• cd /staff/diniz/pub.
A textbook for a graduate or undergraduate course in adaptive signal processing, for students with previous background in the basic principles of digital signal processing and stochastic processes. Focuses on describing a large number of algorithms rather than adapting notation and derivations, and shows how they work in a finite-precision implementation. Annotation c. by Book News, Inc., Portland, Or.
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More About This Textbook
Overview
The field of digital signal processing has developed considerably in the last two decades. This development is related to the growth of available technologies for implementing digital signal processing algorithms. If accurate information on the signals to be processed is available, the designer can easily choose the most appropriate algorithm to process the signal. Fixed algorithms do not process efficiently signals whose statistical properties are unknown. The solution is to use an adaptive filter that automatically changes its characteristics by optimizing its internal parameters. Adaptive filtering algorithms are essential in many statistical signal processing applications.
Adaptive Filtering: Algorithms and Practical Implementation is a concise presentation of adaptive filtering, covering as many algorithms as possible while avoiding adapting notations and derivations related to the different algorithms. Furthermore, the book points out the algorithms which really work in a finite-precision implementation, and provides easy access to the working algorithms for the practicing engineer.
Adaptive Filtering: Algorithms and Practical Implementation may be used as the principal text for courses on the subject, and serves as an excellent reference for professional engineers and researchers in the field.
The author has prepared master transparencies, Matlab routines and an errata file for the book which may be obtained via ftp as follows:
• ftp lps.ufrj.br
• login: anonymous
• password: your email address
• cd /staff/diniz/pub.
Editorial Reviews
Booknews
A textbook for a graduate or undergraduate course in adaptive signal processing, for students with previous background in the basic principles of digital signal processing and stochastic processes. Focuses on describing a large number of algorithms rather than adapting notation and derivations, and shows how they work in a finite-precision implementation. Annotation c. by Book News, Inc., Portland, Or.Product Details
Related Subjects
Table of Contents
Preface. 1. Introduction to Adaptive Filtering. 2. Fundamentals of Adaptive Filtering. 3. The Least-Mean- Square (LMS) Algorithm. 4. LMS-Based Algorithms. 5. Conventional RLS Adaptive Filter. 6. Adaptive Lattice-Based RLS Algorithms. 7. Fast Transversal RLS Algorithms. 8. QR-Decomposition-Based RLS Filters. 9. Adaptive IIR Filters. Index.