Introduction to Random Signals and Applied KalmanFiltering with Matlab Exercises 4th Edition
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More About This Title Introduction to Random Signals and Applied KalmanFiltering with Matlab Exercises 4th Edition
English
The Fourth Edition to the Introduction of Random Signals and Applied Kalman Filtering is updated to cover innovations in the Kalman filter algorithm and the proliferation of Kalman filtering applications from the past decade. The text updates both the research advances in variations on the Kalman filter algorithm and adds a wide range of new application examples. Several chapters include a significant amount of new material on applications such as simultaneous localization and mapping for autonomous vehicles, inertial navigation systems and global satellite navigation systems.
English
Robert Grover Brown and Patrick Y. C. Hwang are the authors of Introduction to Random Signals and Applied Kalman Filtering with Matlab Exercises, 4th Edition, published by Wiley.
English
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
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
