Adaptive 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.
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A Random Variables
B Linear Algebra
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adaptive filters antenna approximations argument assume assumption beamformer channel CHAPTER coefficients column vector complex complex-valued Computer Projects Consider convergence corresponding cost function covariance matrix criterion defined denote derived e-NLMS ea(i eigenvalues EMSE equivalent estimation error estimation problem estimation theory Euclidean norm evaluate expression factor Gaussian random variables given Hermitian Hermitian matrix implementation independent input inverse iteration Kalman filter least-squares problem Lemma linear equalizer linear estimator linear least-mean-squares estimator LMS algorithm mean-square error minimizing noise normal equations notation Observe obtained optimal estimator output plot positive-definite positive-definite matrix Prob QR decomposition real-valued recursion regressors result row vector satisfy scalar Schur complement SECTION sequence Show signal small step-sizes solution solving steady-state stochastic stochastic-gradient subband symbols theorem transformed transient UiWi-i unitary unitary matrix update variance relation Verify weight vector Wi-i zero