Bayesian Extended Target Tracking with Automotive Radar using Learned Spatial Distribution Models

Abstract

We apply the concept of random set cluster processes in combination with a learned measurement model to extended target tracking. The spatial distribution of measurements generated by a target vehicle is learned via a variational Gaussian mixture (VGM) model. The VGM is then interpreted as the measurement likelihood of a Multi-Bernoulli (MB) distribution. We derive a closed-form Bayesian recursion for tracking an extended target by the use of random set cluster process. This formulation is particularly successful for sparse and noisy measurements, and is applied to automotive Radio Detection and Ranging (RADAR) detections. Last, we provide a large-scale evaluation of our approach based on the data published in the Nuscenes data set.