Extended object tracking using hierarchical truncation model with partial-view measurements
Paper in proceeding, 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.

autonomous driving

object tracking

random matrix

Bayesian filtering

Automotive radar

extended object


Yuxuan Xia

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Pu Wang

Mitsubishi Electric Research Laboratories

Karl Berntorp

Mitsubishi Electric Research Laboratories

Hassan Mansour

Mitsubishi Electric Research Laboratories

Petros Boufounos

Mitsubishi Electric Research Laboratories

Philip V. Orlik

Mitsubishi Electric Research Laboratories

Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop

2151870X (eISSN)

Vol. 2020 June 9104388
9781728119465 (ISBN)

2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)
Hangzhou, China,

Subject Categories

Embedded Systems

Control Engineering

Signal Processing



More information

Latest update

1/4/2021 1