Textbook

Introduction to Random Signals and Applied Kalman Filtering with Matlab Exercises, 4th Edition

Robert Grover Brown, Patrick Y. C. Hwang

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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.

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Table of Contents

PART 1: RANDOM SIGNALS BACKGROUND
Chapter 1 Probability and Random Variables: A Review
Chapter 2 Mathematical Description of Random Signals
Chapter 3 Linear Systems Response, State-space Modeling and Monte Carlo Simulation
PART 2: KALMAN FILTERING AND APPLICATIONS
Chapter 4 Discrete Kalman Filter Basics
Chapter 5 Intermediate Topics on Kalman Filtering
Chapter 6 Smoothing and Further Intermediate Topics
Chapter 7 Linearization, Nonlinear Filtering and Sampling Bayesian Filters
Chapter 8 the "Go-Free" Concept, Complementary Filter and Aided Inertial Examples
Chapter 9 Kalman Filter Applications to the GPS and Other Navigation Systems
APPENDIX A. Laplace and Fourier Transforms
APPENDIX B. The Continuous Kalman Filter

New To This Edition

Several new chapters have been created by reducing, combining and restructuring older material, with the goal of making the introductory background material more accessible to undergraduates, including:
- New Chapter 2 - Mathematical description of Random Signals and Linear Systems
- New Chapter 3 - State-Space Modeling and Monte Carlo Simulation
- New Chapter 4 - Derivation of the Basic Kalman filter algorithm
- New Chapter 5 - Alternative forms of the Kalman filter
Later chapters will include a significant amount of new material on nonlinear filtering and sampling Bayesian filters, the "Go-Free" concept as related to complementary filtering, and applications such as simultaneous localization and mapping for autonomous vehicles, inertial navigation systems and global satellite navigation systems.

Hallmark Features

• Leading textbook devoted to Kalman filtering. Competing titles are more research-oriented reference books.
• Emphasizes use of computational software, especially MATLAB. Book Companion Site contains problem solution M-files for the end-of-chapter MATLAB problems.