Sensor Fusion and Non-linear Filtering for Automotive Systems

ChalmersX: ChM015x

LEARNING OBJECTIVES

Basics of Bayesian statistics and recursive estimation theory Describe and model common sensors, and their measurements Compare typical motion models used for positioning, in order to know when to use them in practical problems Describe the essential properties of the Kalman filter (KF) and apply it on linear state space models Implement key nonlinear filters in Matlab, in order to solve problems with nonlinear motion and/or sensor models Select a suitable filter method by analysing the properties and requirements in an application

SCHEDULE

Section 1 - Introduction and Primer in statistics: February 12, 2019 at 08:00 UTC Section 2 - Bayesian statistics (Week 1): February 12, 2019 at 08:00 UTC Section 3 - State space models and optimal filters (Week 1): February 12, 2019 at 08:00 UTC

Section 4 - The Kalman filter and its properties (Week 2-3): February 19, 2019 at 08:00 UTC Section 5 - Motion and measurements models (Week 2-3): February 19, 2019 at 08:00 UTC Section 6 - Non-linear filtering (Week 4): March 5, 2019 at 08:00 UTC Section 7 - Particle filter (Week 5) March 12, 2019 at 08:00 UTC