Extended Object Tracking Using Hierarchical Truncation Model With Partial-View Measurements
Yuxuan Xia, Pu Wang, Karl OE Berntorp, Hassan Mansour, Petros T. Boufounos, Philip Orlik
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10 Jun 2020
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.
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.
