Extended Object Tracking Using Hierarchical Truncation Model With Partial-View Measurements
ID:103 View Protection:ATTENDEE Updated Time:2020-08-05 10:17:28 Hits:367 Oral Presentation

Start Time:2020-06-08 14:40(Asia/Shanghai)


Session:S Special Session » SS10Automotive Radar Sensing

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This paper introduces a flexible measurement model, namely, the hierarchical truncated Gaussian, to resemble the spatial distribution of automotive radar measurements on a vehicle and, along with adaptively updating truncation bounds, to account for partial-view measurements caused by object self-occlusion. Built on a random matrix approach, we propose a new state update step together with the 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 from the MathWorks Automated Driving toolbox.
Automotive radar; object tracking; Bayesian filtering; random matrix; autonomous driving
Yuxuan Xia
Chalmers University of Technology, Sweden

Submission Author
Yuxuan Xia Chalmers University of Technology, Sweden
Pu Wang Mitsubishi Electric Research Laboratories (MERL), USA
Karl OE Mitsubishi Electric Research Labs, USA
Hassan Mansour Mitsubishi Electric Research Laboratories, USA
Petros T. Mitsubishi Electric Research Laboratories & Rice University, USA
Philip Orlik Mitsubishi Electric Research Laboratories, USA
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