Adaptive FiltersAdaptive filtering is a topic of immense practical and theoretical value, having applications in areas ranging from digital and wireless communications to biomedical systems. This book enables readers to gain a gradual and solid introduction to the subject, its applications to a variety of topical problems, existing limitations, and extensions of current theories. The book consists of eleven parts?each part containing a series of focused lectures and ending with bibliographic comments, problems, and computer projects with MATLAB solutions. |
Contents
VectorValued Data | 28 |
xiii | 29 |
Optimal Estimator in the Vector Case | 42 |
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adaptive filters algorithm antenna approximations assume assumption beamformer block channel CHAPTER coefficients column vector complex-valued Computer Project Consider convergence cost function covariance matrix criterion data d(i decision device defined denote diagonal e-NLMS eigenvalues EMSE entries estimation error estimation problem estimation theory Euclidean norm evaluate expression Gaussian random variables given implementation independent input iteration Kalman filter learning curve Lemma linear equalizer linear estimator linear least-mean-squares estimator LMS algorithm mean-square error minimizing minimum mean-square error NLMS noise notation observations obtained optimal estimator orthogonal output performance plot positive-definite positive-definite matrix Prob R₂ real-valued recursion regressors result row vector satisfy scalar Schur complement SECTION sequence Show signal small step-sizes solution steady-state steepest-descent method subband symbols u₁ uncorrelated update variance relation Verify w₁ weight estimate weight vector zero