Discrete Stochastic Processes and Optimal Filtering, Second Edition

Optimal filtering applied to stationary and non-stationary signals provides the most efficient means of dealing with problems arising from the extraction of noise signals. Moreover, it is a fundamental feature in a range of applications, such as in navigation in aerospace and aeronautics, filter processing in the telecommunications industry, etc. This book provides a comprehensive overview of this area, discussing random and Gaussian vectors, outlining the results necessary for the creation of Wiener and adaptive filters used for stationary signals, as well as examining Kalman filters which are used in relation to non-stationary signals. Exercises with solutions feature in each chapter to demonstrate the practical application of these ideas using MATLAB.Content:
Chapter 1 Random Vectors (pages 1–61):
Chapter 2 Gaussian Vectors (pages 63–91):
Chapter 3 Introduction to Discrete Time Processes (pages 93–138):
Chapter 4 Estimation (pages 139–176):
Chapter 5 The Wiener Filter (pages 177–193):
Chapter 6 Adaptive Filtering: Algorithm of the Gradient and the LMS (pages 195–234):
Chapter 7 The Kalman Filter (pages 235–279):