Discrete Random Signal Processing and Filtering Primer with MATLAB

ISBN: 9781420089332 出版年：2018 页码：296 Poularikas, Alexander D CRC Press

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(R) use Appendix B: Matrix analysis Appendix C: Lagrange multiplier method