Extended Object Tracking Using Hierarchical Truncation Model with Partial-View Measurements
Yuxuan Xia, Pu Perry Wang, Karl Berntorp, Hassan Mansour, Petros T. Boufounos, Philip V. Orlik · 2020
This paper introduces the hierarchical truncated Gaussian model in representing automotive radar measurements for extended object tracking. The model aims at a flexible spatial distribution with adaptive truncation bounds to account for partial-view measurements caused by self-occlusion. Built on a random matrix approach, we propose a new state update step together with an adaptively update of the truncation bounds. This is achieved by introducing spatial-domain pseudo measurements and by aggregating partial-view measurements over consecutive time-domain scans. The effectiveness of the proposed algorithm is verified on a synthetic dataset and an independent dataset generated using the MathWorks Automated Driving toolbox.