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Extended Object Tracking using Hierarchical Truncation Measurement Model with Automotive Radar

Citation Author(s):
Pu (Perry) Wang, Karl Berntorp, Toshiaki Koike-Akino, Hassan Mansour, Milutin Pajovic, Petros Boufounos, Philip Orlik
Submitted by:
Yuxuan Xia
Last updated:
13 May 2020 - 9:42pm
Document Type:
Presentation Slides
Document Year:
2020
Event:
Presenters Name:
Yuxuan Xia
Paper Code:
4348

Abstract 

Abstract: 

Motivated by real-world automotive radar measurements that are distributed around object (e.g., vehicles) edges with a certain volume, a novel hierarchical truncated Gaussian measurement model is proposed to resemble the underlying spatial distribution of radar measurements. With the proposed measurement model, a modified random matrix-based extended object tracking algorithm is developed to estimate both kinematic and extent states. In particular, a new state update step and an online bound estimation step are proposed with the introduction of pseudo measurements. The effectiveness of the proposed algorithm is verified in simulations.

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Presentation slides of ICASSP 2020 paper "Extended Object Tracking using Hierarchical Truncation Measurement Model with Automotive Radar".

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ICASSP 2020.pdf

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