Introduction to Random Signals and Applied Kalman Filtering: with Matlab Exercises, you will download this book in this post.
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.You May Like: Advanced Engineering Electromagnetics by Constantine A. Balanis
Table of Contents
PART 1: RANDOM SIGNALS BACKGROUND- Probability and Random Variables: A Review
- Mathematical Description of Random Signals
- Linear Systems Response, State-space Modeling and Monte Carlo Simulation
- Discrete Kalman Filter Basics
- Intermediate Topics on Kalman Filtering
- Smoothing and Further Intermediate Topics
- Linearization, Nonlinear Filtering and Sampling Bayesian Filters
- The "Go-Free" Concept, Complementary Filter and Aided Inertial Examples
- Kalman Filter Applications to the GPS and Other Navigation Systems
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