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Gabor [1] was the first to conceive the idea of a nonlinear adaptive filter in 1954 using a Volterra series. The first algorithm used to design a linear adaptive filter is the ubiquitous least-mean-square (LMS) algorithm developed by Widrow and Hoff [2].

• NEW - Revision—Consolidates the mathematical treatment of linear adaptive filters. • Improves the presentation of material on statistical LMS theory and statistical RLS theory. • Expands the treatment of normalized LMS filters, and introduces the more general case of affine projection filters. • Introduces sub-band adaptive filters. • Repositions the teaching of Kalman filters after the treatment of RLS filters, thereby enhancing the unified treatment of square-root adaptive filters and order recursive adaptive filters. • NEW - In-depth treatment of adaptive filters in a highly readable and understandable fashion.

• NEW - Major revision of the MATLAB codes for the computer experiments—Available on the web. • NEW - Website (• — • Includes a highly intensive research program on the applications of adaptive filters and neural networks to signal processing and communications with emphasis on: space-time wireless communications, radar surveillance, and chaotic signal processing. • Extensive use of illustrative examples. • Extensive use of MATLAB experiments—Illustrates the practical realities and intricacies of adaptive filters, the codes for which can be downloaded from the Web.

• Extensive bibliography of the subject. • Revision—Consolidates the mathematical treatment of linear adaptive filters. • Improves the presentation of material on statistical LMS theory and statistical RLS theory. • Expands the treatment of normalized LMS filters, and introduces the more general case of affine projection filters.

• Introduces sub-band adaptive filters. • Repositions the teaching of Kalman filters after the treatment of RLS filters, thereby enhancing the unified treatment of square-root adaptive filters and order recursive adaptive filters.

• In-depth treatment of adaptive filters in a highly readable and understandable fashion. • Major revision of the MATLAB codes for the computer experiments—Available on the web. • Website (• — • Includes a highly intensive research program on the applications of adaptive filters and neural networks to signal processing and communications with emphasis on: space-time wireless communications, radar surveillance, and chaotic signal processing. Table of Contents Background and Overview. Stochastic Processes and Models. Wiener Filters. Linear Prediction.

Method of Steepest Descent. Least-Mean-Square Adaptive Filters.

Normalized Least-Mean-Square Adaptive Filters. Transform-Domain and Sub-Band Adaptive Filters. Method of Least Squares. Recursive Least-Square Adaptive Filters.

Kalman Filters as the Unifying Bases for RLS Filters. Square-Root Adaptive Filters.

Order-Recursive Adaptive Filters. Finite-Precision Effects. Tracking of Time-Varying Systems. Adaptive Filters Using Infinite-Duration Impulse Response Structures. Blind Deconvolution.

Back-Propagation Learning. Complex Variables. Differentiation with Respect to a Vector. Method of Lagrange Multipliers. Estimation Theory.

Rotations and Reflections. Complex Wishart Distribution. Principal Symbols.