Adaptive Filter Theory"Adaptive Filter Theory" looks at both the mathematical theory behind various linear adaptive filters with finite-duration impulse response (FIR) and the elements of supervised neural networks. Up-to-date and in-depth treatment of adaptive filters develops concepts in a unified and accessible manner. This highly successful book provides comprehensive coverage of adaptive filters in a highly readable and understandable fashion. Includes an extensive use of illustrative examples; and MATLAB experiments, which illustrate the practical realities and intricacies of adaptive filters, the codes for which can be downloaded from the Web. Covers a wide range of topics including Stochastic Processes, Wiener Filters, and Kalman Filters. For those interested in learning about adaptive filters and the theories behind them. |
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Contents
INTRODUCTION | 1 |
STATIONARY DISCRETETIME STOCHASTIC PROCESSES | 44 |
WIENER FILTER THEORY | 100 |
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Common terms and phrases
adaptive beamformer adaptive equalizer adaptive filter adaptive transversal filter autoregressive average backward prediction errors backward prediction-error filter beamformer bm(n compute convergence correlation matrix corresponding cross-correlation cross-correlation vector data matrix denote desired response d(n deterministic correlation matrix discrete-time stochastic process eigenvectors elements ensemble-averaged filter of order fm(n follows forward prediction-error filter FTF algorithm gain vector Givens rotation Hence Hermitian initial input data inverse Jmin Kailath Kalman algorithm Kalman filter lattice predictor linear prediction LMS algorithm mean-squared error minimum mean-squared noise normal equation optimum orthogonal output problem process u(n recursive recursive LSL algorithm reflection coefficients sample Section sequence side of Eq signal solution squares stationary step-size parameter stochastic process substituting Eq systolic array tap inputs tap-input vector tap-weight vector transfer function triangular unit circle unitary matrix updated v₁(n v₂(n vector u(n weight vector weight-error Wiener filter z-transform zero mean Σ Σ