Ebook: Adaptive Filtering: Algorithms and Practical Implementation

The field of Digital Signal Processing has developed so fast in the last two decades that it can be found in the graduate and undergraduate programs of most universities. This development is related to the growing available techno­ logies for implementing digital signal processing algorithms. The tremendous growth of development in the digital signal processing area has turned some of its specialized areas into fields themselves. If accurate information of the signals to be processed is available, the designer can easily choose the most appropriate algorithm to process the signal. When dealing with signals whose statistical properties are unknown, fixed algorithms do not process these signals efficiently. The solution is to use an adaptive filter that automatically changes its characteristics by optimizing the internal parameters. The adaptive filtering algorithms are essential in many statistical signal processing applications. Although the field of adaptive signal processing has been subject of research for over three decades, it was in the eighties that a major growth occurred in research and applications. Two main reasons can be credited to this growth, the availability of implementation tools and the appearance of early textbooks exposing the subject in an organized form. Presently, there is still a lot of activities going on in the area of adaptive filtering. In spite of that, the theor­ etical development in the linear-adaptive-filtering area reached a maturity that justifies a text treating the various methods in a unified way, emphasizing the algorithms that work well in practical implementation.

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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.

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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.

Content:
Front Matter....Pages i-xv
Introduction to Adaptive Filtering....Pages 1-14
Fundamentals of Adaptive Filtering....Pages 15-69
The Least-Mean-Square (LMS) Algorithm....Pages 71-131
LMS-Based Algorithms....Pages 133-181
Conventional RLS Adaptive Filter....Pages 183-236
Adaptive Lattice-Based RLS Algorithms....Pages 237-287
Fast Transversal RLS Algorithms....Pages 289-309
QR-Decomposition-Based RLS Filters....Pages 311-376
Adaptive IIR Filters....Pages 377-436
Back Matter....Pages 437-443

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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.

Content:
Front Matter....Pages i-xv
Introduction to Adaptive Filtering....Pages 1-14
Fundamentals of Adaptive Filtering....Pages 15-69
The Least-Mean-Square (LMS) Algorithm....Pages 71-131
LMS-Based Algorithms....Pages 133-181
Conventional RLS Adaptive Filter....Pages 183-236
Adaptive Lattice-Based RLS Algorithms....Pages 237-287
Fast Transversal RLS Algorithms....Pages 289-309
QR-Decomposition-Based RLS Filters....Pages 311-376
Adaptive IIR Filters....Pages 377-436
Back Matter....Pages 437-443
....