Discrete Random Signal Processing and Filtering Primer with MATLAB (Hardcover)

Fourier analysis of signals Introduction FT Sampling of signals Discrete-time FT (DTFT) DFT Resolution Continuous linear systems Discrete systems?linear difference equations Detrending Random variables, sequences, and stochastic processes Random signals and distributions Averages Stationary processes Wiener?Khinchin relations Filtering random processes PDFs Estimators Confidence intervals Nonparametric (classical) spectrums estimation Periodogram and correlogram spectra estimators Daniell periodogram Bartlett periodogram Blackman?Tukey (BT) method Welch method Proposed modified methods for Welch periodogram Parametric and other methods for spectra estimation AR, MA, and ARMA models Yule?Walker (YW) equations Least-squares (LS) method and linear prediction Minimum variance (MV) method Model order Levinson?Durbin algorithm Maximum entropy method Spectrums of segmented signals Eigenvalues and eigenvectors of matrices Optimal filtering?Wiener filters Mean square error (MSE) FIR Wiener filter Wiener solution?orthogonal principle Wiener filtering examples Adaptive filtering?LMS algorithm Introduction LMS algorithm Examples using the LMS algorithm Properties of the LMS method Adaptive filtering with variations of LMS algorithm. Sign algorithms Normalized LMS (NLMS) algorithm Variable step-size LMS algorithm (VSLMS) Leaky LMS algorithm Linearly constrained LMS algorithm Self-correcting adaptive filtering (SCAF) Transform domain adaptive LMS filtering Convergence in transform domain of the adaptive LMS filtering Error-normalized LMS algorithm Nonlinear filtering Introduction Statistical preliminaries Mean filter Median filter Trimmed-type mean filter L-filters Ranked-order statistic filter Edge-enhancement filters R-filters Appendix A: Suggestions and explanations for MATLAB® use Appendix B: Matrix analysis Appendix C: Lagrange multiplier method