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

About the Author

Robert Grover Brown, Professor Emeritus, Iowa State University.

Patrick Y. C. Hwang, Rockwell Collins, Inc.