Extended Object Tracking Using Hierarchical Truncation Measurement Model with Automotive Radar
Paper i proceeding, 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.

extended object

object tracking

random matrix

autonomous driving

Bayesian filtering

Automotive radar

Författare

Yuxuan Xia

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

Pu Wang

Mitsubishi Electric Research Laboratories

Karl Berntorp

Mitsubishi Electric Research Laboratories

Toshiaki Koike-Akino

Mitsubishi Electric Research Laboratories

Hassan Mansour

Mitsubishi Electric Research Laboratories

Milutin Pajovic

Mitsubishi Electric Research Laboratories

Petros Boufounos

Mitsubishi Electric Research Laboratories

Philip V. Orlik

Mitsubishi Electric Research Laboratories

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

15206149 (ISSN)

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Barcelona, Spain,

Ämneskategorier

Telekommunikation

Sannolikhetsteori och statistik

Signalbehandling

DOI

10.1109/ICASSP40776.2020.9054614

Mer information

Skapat

2020-12-23