EE3901/EE5901 Sensor Technologies
Chapter 2 — Sensor fusion and the Kalman filter

Written by A/Prof Bronson Philippa and Laurance Papale

College of Science and Engineering, James Cook University

Sensor fusion is the process of combining information from multiple sensors to determine the state of a system. In this chapter, we will study the Kalman filter, which is one of the most famous and important sensor fusion algorithms. We will then explore two methods to extend the Kalman filter to arbitrary, non-linear systems. Specifically we study the Extended Kalman Filter and the Unscented Kalman Filter.

Chapter 2 Notes

Work through these notes before the lecture.

Chapter 2 Tutorial

Questions for you to develop your problem solving skills.

Reading material

How a Kalman filter works (in pictures), by Tim Babb

This is a friendly introduction to the Kalman filter. It is highly recommended.

Reference material (optional reading)

An elementary introduction to Kalman filtering, by Yan Pei et al.

This is an academic paper (so you will need to be on campus or use the browser extension "EZProxy Redirect" to bounce it through the university library). It gives a more rigorous introduction to the topic.