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Distributedmulti-target tracking with labeled multi-Bernoulli filter considering efficient label matching
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Changwen DING, Chuntao SHAO, Siteng ZHOU, Di ZHOU?1, Runle DU, Jiaqi LI. Distributedmulti-target tracking with labeled multi-Bernoulli filter considering efficient label matching[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .
@article{title="Distributedmulti-target tracking with labeled multi-Bernoulli filter considering efficient label matching",
author="Changwen DING, Chuntao SHAO, Siteng ZHOU, Di ZHOU?1, Runle DU, Jiaqi LI",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2400582"
}
%0 Journal Article
%T Distributedmulti-target tracking with labeled multi-Bernoulli filter considering efficient label matching
%A Changwen DING
%A Chuntao SHAO
%A Siteng ZHOU
%A Di ZHOU?1
%A Runle DU
%A Jiaqi LI
%J Journal of Zhejiang University SCIENCE C
%V -1
%N -1
%P
%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2400582
TY - JOUR
T1 - Distributedmulti-target tracking with labeled multi-Bernoulli filter considering efficient label matching
A1 - Changwen DING
A1 - Chuntao SHAO
A1 - Siteng ZHOU
A1 - Di ZHOU?1
A1 - Runle DU
A1 - Jiaqi LI
J0 - Journal of Zhejiang University Science C
VL - -1
IS - -1
SP -
EP -
%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2400582
Abstract: We proposed a distributed labeled multi-Bernoulli (LMB) filter based on an efficient label matching method. Conventional distributed LMB filter fusion has the premise that the labels among local densities have already been matched. However, considering that the label space of each local posterior is independent, such a premise is not practical in many applications. To achieve the distributed fusion practically, we proposed an efficient label matching method derived from the divergence of arithmetic average (AA) mechanism, and subsequently, the label-wise LMB fusion is performed according to the matching result. Compared with the existing label matching methods, this proposed method shows higher performance, especially in low detection probability scenarios. Moreover, to guarantee the consistency and completeness of the fusion outcome, the overall fusion procedure is designed into the following four stages: pre-fusion, label determination, posterior complement, and uniqueness check stages. The performance of the proposed label matching distributed LMB fusion is demonstrated in a challenging nonlinear bearings-only multi-target tracking (MTT) scenario.
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