Extended Object Tracking Using Hierarchical Truncation Measurement Model with Automotive Radar
Yuxuan Xia, P. Wang, Karl Berntorp, Toshiaki Koike–Akino, Hassan Mansour, Milutin Pajovic, Petros T. Boufounos, Phillip V. Orlik · 2020
Motivated by real-world automotive radar measurements that are distributed around object (e.g., vehicles) edges with a certain volume, a novel hierarchical truncated Gaussian measurement model is proposed to resemble the underlying spatial distribution of radar measurements. With the proposed measurement model, a modified random matrix-based extended object tracking algorithm is developed to estimate both kinematic and extent states. In particular, a new state update step and an online bound estimation step are proposed with the introduction of pseudo measurements. The effectiveness of the proposed algorithm is verified in simulations.