This essentially self-contained, deliberately compact, and user-friendly textbook is designed for a first, one-semester course in statistical signal analysis for a broad audience of students in engineering and the physical sciences. The emphasis throughout is on fundamental concepts and relationships in the statistical theory of stationary random signals, explained in a concise, yet fairly rigorous presentation.
Topics and Features: Fourier series and transforms are developed from scratch, emphasizing the time-domain vs. frequency-domain duality. Basic concepts of probability theory, laws of large numbers, the stability of fluctuations law, and statistical parametric inference procedures are presented. Introduction of the fundamental concept of a stationary random signal and its autocorrelation structure. Many diverse examples as well as end-of-chapter problems and exercises.
Developed by the author over the course of several years of classroom use, A First Course in Statistics for Signal Analysis may be used by junior/senior undergraduates or graduate students in electrical, systems, computer, and biomedical engineering, as well as the physical sciences. The work is also an excellent resource of educational and training material for scientists and engineers working in research laboratories. TOC:Introduction.- Notation.- Description of Signals.- Spectral Representation of Deterministic Signals: Fourier Series and Transforms.- Random Quantities and Random Vectors.- Stationary Signals.- Power Spectra of Stationary Signals.- Transmission of Stationary Signals through Linear Systems.- Optimization of Signal-to-Noise Ratio in Linear Systems.- Gaussian Signals, Correlation Matrices, and Sample Path Properties.- Discrete Signals and Their Computer Simulations.- Bibliographical Comments