Full Text:   <1593>

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CLC number: TN713

On-line Access: 2021-08-17

Received: 2020-05-01

Revision Accepted: 2020-07-02

Crosschecked: 2021-07-14

Cited: 0

Clicked: 2771

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Yun Zhu

https://orcid.org/0000-0001-7009-7627

Xiaojun Wu

https://orcid.org/0000-0002-7779-553X

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Frontiers of Information Technology & Electronic Engineering  2021 Vol.22 No.8 P.1114-1126

http://doi.org/10.1631/FITEE.2000209


A random finite set based joint probabilistic data association filter with non-homogeneous Markov chain


Author(s):  Yun Zhu, Shuang Liang, Xiaojun Wu, Honghong Yang

Affiliation(s):  Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi’an 710062, China; more

Corresponding email(s):   yunzhu@snnu.edu.cn, xjwu@snnu.edu.cn

Key Words:  Target tracking, Filtering theory, Random finite set theory, Bayes methods, Markov chain


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.

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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.

基于随机有限集的非齐次马尔可夫链联合概率数据关联滤波器

朱昀1,2,梁爽3,吴晓军1,2,杨红红1,2
1陕西师范大学现代教学技术教育部重点实验室,中国西安市,710062
2陕西师范大学计算机科学学院,中国西安市,710119
3西安电子科技大学前沿交叉研究院,中国西安市,710071
摘要:提出一种启发式方法,通过随机有限集理论优化数据关联跟踪算法的后验密度。具体而言,提出一种改进的联合概率数据关联滤波方法,即最近邻集合联合概率数据关联方法(NNSJPDA)。为提高边缘化的准确性,利用一种基于Kullback–Leibler散度的最近邻方法,对所有可能的数据关联事件中的目标标签进行转换。此外,进一步考虑目标标签向量的分布。后验密度转换后,可得到目标标签向量的转移矩阵。该转移矩阵随时间变化,使得目标标签向量分布的传播遵循非齐次马尔可夫链。证明了该链本质上是双随机的,并推导了相应定理。通过举例和仿真,验证了所提方法的有效性。本文结果可推广到相同随机有限集框架下的其他数据关联方法。

关键词:目标跟踪;滤波理论;随机有限集理论;贝叶斯方法;马尔可夫链

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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