Description
The emergence of affordable micro sensors, such as MEMS Inertial Measurement Systems, which are being applied in embedded systems and Internet-of-Things devices, has brought techniques such as Kalman Filtering, capable of combining information from multiple sensors or sources, to the interest of students and hobbyists. This will book will develop just the necessary background concepts, helping a much wider audience of readers develop an understanding and intuition that will enable them to follow the explanation for the Kalman Filtering algorithm
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
