304 pages | 110 B/W Illus.
Engineers in all fields will appreciate a practical guide that combines several new effective MATLAB® problem-solving approaches and the very latest in discrete random signal processing and filtering.
Numerous Useful Examples, Problems, and Solutions – An Extensive and Powerful Review
Written for practicing engineers seeking to strengthen their practical grasp of random signal processing, Discrete Random Signal Processing and Filtering Primer with MATLAB provides the opportunity to doubly enhance their skills. The author, a leading expert in the field of electrical and computer engineering, offers a solid review of recent developments in discrete signal processing. The book also details the latest progress in the revolutionary MATLAB language.
A Practical Self-Tutorial That Transcends Theory
The author introduces an incremental discussion of signal processing and filtering, and presents several new methods that can be used for a more dynamic analysis of random digital signals with both linear and non-linear filtering. Ideal as a self-tutorial, this book includes numerous examples and functions, which can be used to select parameters, perform simulations, and analyze results. This concise guide encourages readers to use MATLAB functions – and those new ones introduced as Book MATLAB Functions – to substitute many different combinations of parameters, giving them a firm grasp of how much each parameter affects results.
Much more than a simple review of theory, this book emphasizes problem solving and result analysis, enabling readers to take a hands-on approach to advance their own understanding of MATLAB and the way it is used within signal processing and filtering.
Fourier analysis of signals
Sampling of signals
Discrete-time FT (DTFT)
Continuous linear systems
Discrete systems—linear difference equations
Random variables, sequences, and stochastic processes
Random signals and distributions
Filtering random processes
Nonparametric (classical) spectrums estimation
Periodogram and correlogram spectra estimators
Blackman–Tukey (BT) 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
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
Examples using the LMS algorithm
Properties of the LMS method
Adaptive filtering with variations of LMS algorithm.
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
Trimmed-type mean filter
Ranked-order statistic filter
Appendix A: Suggestions and explanations for MATLAB® use
Appendix B: Matrix analysis
Appendix C: Lagrange multiplier method