286 / 2020-01-05 15:05:00

Extended Object Tracking Using Hierarchical Truncation Model With Partial-View Measurements

Automotive radar; object tracking; Bayesian filtering; random matrix; autonomous driving

IEEE SAM 2020: Special Sessions > Automotive Radar Sensing

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.