ABSTRACT

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

part I|52 pages

Background

chapter Chapter 1|21 pages

System Models and Random Variables

chapter Chapter 2|20 pages

Multiple Random Sequences Considered Jointly

part II|48 pages

Where Does Kalman Filtering Apply and What Does It Intend to Do?

part III|50 pages

Examples in MATLAB®

chapter Chapter 9|17 pages

Univariate Example of Kalman Filter in MATLAB®

chapter Chapter 10|20 pages

Multivariate Example of Kalman Filter in MATLAB®

part IV|45 pages

Kalman Filtering Application to IMUs