Random Signals, Noise and Filtering develops the theory of random processes and its application to the study of systems and analysis of random data. The text covers three important areas: (1) fundamentals and examples of random process models, (2) applications of probabilistic models: signal detection, and filtering, and (3) statistical estimation--measurement and analysis of random data to determine the structure and parameter values of probabilistic models. This volume by Breipohl and Shanmugan offers the only one-volume treatment of the fundamentals of random process models, their applications, and data analysis.
Table of Contents
Preface and Introduction.
Review of Probability and Random Variables.
Random Processes and Sequences.
Response of Systems to Random Inputs.
Special Classes of Random Processes.
Linear Minimum MSE Filtering.
Estimating Parameters of Random Processes from Data.