Kalman Filter Based Extended Object Tracking with a Gaussian Mixture Spatial Distribution Model

Abstract

Extended object tracking methods are often based on the assumption that the measurements are uniformly distributed on the target object. However, this assumption is often invalid for applications using automotive radar or lidar data. Instead, there is a bias towards the side of the object that is visible to the sensor. To handle this challenge, we employ a Gaussian Mixture (GM) density to model a more detailed measurement distribution across the surface and extend a recent Kalman filter based elliptic object tracker called MEM-EKF* to get a closed-form solution for the measurement update. An evaluation of the proposed approach compared with classic elliptic trackers and a recent truncation-based approach is conducted on simulated data.