Random Process: Filtering, Estimation, and Detection

Author: Lude Mann / Qiu Tianshuang / Li Ting / Bi Yingwei, etc.
Publisher:
Publish Date: 2005-02-01
Features: This book comprehensively introduces classical linear and nonlinear system analysis techniques and hypothesis testing techniques in the theory of stochastic processes. It elaborates on optimal estimation methods and optimal decision rules for classification, describes criteria and evaluation methods for performance evaluation. Additionally, the book delves into filtering, estimation, and detection problems of random processes in noise. The book is divided into 10 chapters, including probability space and probability, random variables, estimation of random variables, random processes, random processes through linear systems, random processes through nonlinear systems, optimal linear Wiener filter, optimal linear Kalman filter, detection theory for discrete observation signals, and detection theory for continuous observation signals. While conducting in-depth analysis around the theme, the book provides numerous application examples and exercises, making it an easy-to-understand and highly applicable book. It can serve as a textbook for graduate students and senior undergraduate students, as well as a reference for engineering technicians in related fields for self-study.