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CLC number: TN713
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2021-07-14
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Citations: Bibtex RefMan EndNote GB/T7714
A random finite set based joint probabilistic data association filter with non-homogeneous Markov chain
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Yun Zhu, Shuang Liang, Xiaojun Wu, Honghong Yang. A random finite set based joint probabilistic data association filter with non-homogeneous Markov chain[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(8): 1114-1126.
@article{title="A random finite set based joint probabilistic data association filter with non-homogeneous Markov chain",
author="Yun Zhu, Shuang Liang, Xiaojun Wu, Honghong Yang",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="22",
number="8",
pages="1114-1126",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000209"
}
%0 Journal Article
%T A random finite set based joint probabilistic data association filter with non-homogeneous Markov chain
%A Yun Zhu
%A Shuang Liang
%A Xiaojun Wu
%A Honghong Yang
%J Frontiers of Information Technology & Electronic Engineering
%V 22
%N 8
%P 1114-1126
%@ 2095-9184
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000209
TY - JOUR
T1 - A random finite set based joint probabilistic data association filter with non-homogeneous Markov chain
A1 - Yun Zhu
A1 - Shuang Liang
A1 - Xiaojun Wu
A1 - Honghong Yang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 22
IS - 8
SP - 1114
EP - 1126
%@ 2095-9184
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000209
Abstract: We demonstrate a heuristic approach for optimizing the posterior density of the data association tracking algorithm via the random finite set (RFS) theory. Specifically, we propose an adjusted version of the joint probabilistic data association (JPDA) filter, known as the nearest-neighbor set JPDA (NNSJPDA). The target labels in all possible data association events are switched using a novel nearest-neighbor method based on the Kullback–Leibler divergence, with the goal of improving the accuracy of the marginalization. Next, the distribution of the target-label vector is considered. The transition matrix of the target-label vector can be obtained after the switching of the posterior density. This transition matrix varies with time, causing the propagation of the distribution of the target-label vector to follow a non-homogeneous markov chain. We show that the chain is inherently doubly stochastic and deduce corresponding theorems. Through examples and simulations, the effectiveness of NNSJPDA is verified. The results can be easily generalized to other data association approaches under the same RFS framework.
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