Introduction to Random Signals and Applied Kalman Filtering: With MATLAB Exercises, 4th Edition

Advances in computers and personal navigation systems have greatly expanded the applications of Kalman filters. A Kalman filter uses information about noise and system dynamics to reduce uncertainty from noisy measurements. Common applications of Kalman filters include such fast-growing fields as autopilot systems, battery state of charge (SoC) estimation, brain-computer interface, dynamic positioning, inertial guidance systems, radar tracking, and satellite navigation systems.

Brown and Hwang's bestselling textbook introduces the theory and applications of Kalman filters for senior undergraduates and graduate students. This revision updates both the research advances in variations on the Kalman filter algorithm and adds a wide range of new application examples. The book emphasizes the application of computational software tools such as MATLAB. The companion website includes M-files to assist students in applying MATLAB to solving end-of-chapter homework problems.

Table of Contents

PART 1: RANDOM SIGNALS BACKGROUND

1 Probability and Random Variables: A Review

2 Mathematical Description of Random Signals

3 Linear Systems Response, State-Space Modeling, and Monte Carlo Simulation

PART 2: KALMAN FILTERING AND APPLICATIONS

4 Discrete Kalman Filter Basics

5 Intermediate Topics on Kalman Filtering

6 Smoothing and Further Intermediate Topics

7 Linearization, Nonlinear Filtering, and Sampling Bayesian Filters

8 The "Go-Free" Concept, Complementary Filter, and Aided Inertial Examples

9 Kalman Filter Applications to the GPS and Other Navigation Systems