Extended Object Tracking Using Hierarchical Truncation Measurement Model with Automotive Radar

Yuxuan Xia, who was an intern at MERL for this work, presented his paper titled "Extended Object Tracking Using Hierarchical Truncation Measurement Model With Automotive Radar," for the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), held virtually May 4-8 2020. The paper was co-authored with MERL researchers Pu (Perry) Wang, Karl Berntorp, Toshiaki Koike-Akino, Hassan Mansour, Milutin Pajovic, Petros T. Boufounos, and Philip V. Orlik.
Paper: ieeexplore.ieee.org/document/...
www.merl.com/publications/doc...
ABSTRACT: 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.