Learning Kalman filtering with Lego mindstorms

Research shows that, in learning science and engineering, guided project work leads to deeper understanding of theoretical concepts (as well as acquisition of hands-on skills) than the classical approach of textbook reading and attending lectures. In an approach to education based on project work, the role of the teacher is to create a stimulating learning environment and to supervise the students in accomplishing their objectives. The main challenge is to come up with projects that are engaging, diverse, and feasible in view of limited time and resources. In this paper, we describe such a signal processing project. The task is to improve the speed and accuracy characteristics of a sensor by real-time signal processing. It turns out that this is an application of Kalman filtering however the students need to identify a model of the sensor and implement the Kalman filter on a DSP. The project consists of three main tasks: 1) mathematical formalization of the problem, 2) development of solution methods, and 3) implementation and testing of the methods. The testing is done on an inexpensive laboratory setup, using the Lego Mindstorms educational kit in combination with a temperature sensor. The learning outcomes are understanding of model representations, system identification, and state estimation, as well as implementation in MATLAB and C of real-time signal processing algorithms. Possible extensions include data fusion of multiple sensors, adaptive signal processing, and applications to other sensors. 


  1. I. Markovsky. Learning Kalman filtering with Lego Mindstorms. Technical report, Dept. ELEC, Vrije Universiteit Brussel, 2018.
Back to top