Table of Contents
Part I Background
Chapter 1 ■ System Models and Random Variables 3
Chapter 2 ■ Multiple Random Sequences
Chapter 3 ■ Conditional Probability, Bayes’ Rule and Bayesian Estimation 45
Part II Where Does Kalman Filtering Apply and What Does It Intend to Do?
Chapter 4 ■ A Simple Scenario Where Kalman
Chapter 5 ■ General Scenario Addressed by Kalman Filtering and Specific Cases 61
Chapter 6 ■ Arriving at the Kalman Filter Algorithm 75
Chapter 7 ■ Reflecting on the Meaning and Evolution of the Entities in the Kalman Filter Algorithm 87
Part III Examples in MATLAB®
Chapter 8 ■ MATLAB® Function to Implement and Exemplify the Kalman Filter 103
Chapter 9 ■ Univariate Example of Kalman Filter in MATLAB® 113
Chapter 10 ■ Multivariate Example of Kalman Filter in MATLAB® 131
Part IV Kalman Filtering Application to IMUs
Chapter 11 ■ Kalman Filtering Applied to 2-Axis Attitude Estimation from Real IMU Signals 153
Chapter 12 ■ Real-Time Kalman Filtering Application to Attitude Estimation from IMU Signals 179
APPENDIX A □LISTINGS OF THE FILES FOR REAL-TIME IMPLEMENTATION OF THE KALMAN
FILTER FOR ATTITUDE ESTIMATION WITH ROTATIONS IN 2 AXES, 197