Extended object tracking using hierarchical truncation model with partial-view measurements
Paper i 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, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

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

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


Inbäddad systemteknik





Mer information

Senast uppdaterat