Resolvable Cluster Target Tracking Based on the DBSCAN Clustering Algorithm and Labeled RFS

When a sensor can resolve the members in a cluster, it is difficult to accurately track each target due to cooperative interaction among the targets. In this paper, we research the tracking problem of resolvable cluster targets with cooperative interaction. Firstly, we use the stochastic differentia...

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Vydáno v:	IEEE access Ročník 9; s. 43364 - 43377
Hlavní autoři:	Xue, Xirui, Huang, Shucai, Xie, Jiahao, Ma, Jiashun, Li, Ning
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Clustering Clustering algorithms cooperative interaction modeling DBSCAN clustering algorithm Differential equations Equations of state Filtering algorithms Filtering theory Force Formulas (mathematics) IIR filters Mathematical model Prediction algorithms Resolvable cluster target tracking stochastic differential equation Target tracking Tracking problem δ-GLMB filter
ISSN:	2169-3536, 2169-3536
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Abstract	When a sensor can resolve the members in a cluster, it is difficult to accurately track each target due to cooperative interaction among the targets. In this paper, we research the tracking problem of resolvable cluster targets with cooperative interaction. Firstly, we use the stochastic differential equation to model the cluster coordination rules, and the state equation of the single target in the cluster is derived. On this basis, a Bayes recursive filter tracking method based on the combination of the DBSCAN clustering algorithm and the <inline-formula> <tex-math notation="LaTeX">\delta </tex-math></inline-formula>-GLMB filter is proposed. In the <inline-formula> <tex-math notation="LaTeX">\delta </tex-math></inline-formula>-GLMB filter prediction stage, the DBSCAN algorithm is used to determine the cluster where the target is located in real time. Then, the collaborative noise of the target is estimated, which will be used as the input to correct the prediction state of the target. The simulation and experiment results demonstrate the effectiveness of the proposed algorithm when the cluster is splitting, merging, and in reorganization.
AbstractList	When a sensor can resolve the members in a cluster, it is difficult to accurately track each target due to cooperative interaction among the targets. In this paper, we research the tracking problem of resolvable cluster targets with cooperative interaction. Firstly, we use the stochastic differential equation to model the cluster coordination rules, and the state equation of the single target in the cluster is derived. On this basis, a Bayes recursive filter tracking method based on the combination of the DBSCAN clustering algorithm and the <tex-math notation="LaTeX">$\delta $ </tex-math>-GLMB filter is proposed. In the <tex-math notation="LaTeX">$\delta $ </tex-math>-GLMB filter prediction stage, the DBSCAN algorithm is used to determine the cluster where the target is located in real time. Then, the collaborative noise of the target is estimated, which will be used as the input to correct the prediction state of the target. The simulation and experiment results demonstrate the effectiveness of the proposed algorithm when the cluster is splitting, merging, and in reorganization. When a sensor can resolve the members in a cluster, it is difficult to accurately track each target due to cooperative interaction among the targets. In this paper, we research the tracking problem of resolvable cluster targets with cooperative interaction. Firstly, we use the stochastic differential equation to model the cluster coordination rules, and the state equation of the single target in the cluster is derived. On this basis, a Bayes recursive filter tracking method based on the combination of the DBSCAN clustering algorithm and the <inline-formula> <tex-math notation="LaTeX">\delta </tex-math></inline-formula>-GLMB filter is proposed. In the <inline-formula> <tex-math notation="LaTeX">\delta </tex-math></inline-formula>-GLMB filter prediction stage, the DBSCAN algorithm is used to determine the cluster where the target is located in real time. Then, the collaborative noise of the target is estimated, which will be used as the input to correct the prediction state of the target. The simulation and experiment results demonstrate the effectiveness of the proposed algorithm when the cluster is splitting, merging, and in reorganization. When a sensor can resolve the members in a cluster, it is difficult to accurately track each target due to cooperative interaction among the targets. In this paper, we research the tracking problem of resolvable cluster targets with cooperative interaction. Firstly, we use the stochastic differential equation to model the cluster coordination rules, and the state equation of the single target in the cluster is derived. On this basis, a Bayes recursive filter tracking method based on the combination of the DBSCAN clustering algorithm and the [Formula Omitted]-GLMB filter is proposed. In the [Formula Omitted]-GLMB filter prediction stage, the DBSCAN algorithm is used to determine the cluster where the target is located in real time. Then, the collaborative noise of the target is estimated, which will be used as the input to correct the prediction state of the target. The simulation and experiment results demonstrate the effectiveness of the proposed algorithm when the cluster is splitting, merging, and in reorganization.
Author	Li, Ning Xue, Xirui Huang, Shucai Ma, Jiashun Xie, Jiahao
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SubjectTerms	Algorithms Clustering Clustering algorithms cooperative interaction modeling DBSCAN clustering algorithm Differential equations Equations of state Filtering algorithms Filtering theory Force Formulas (mathematics) IIR filters Mathematical model Prediction algorithms Resolvable cluster target tracking stochastic differential equation Target tracking Tracking problem δ-GLMB filter
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Title	Resolvable Cluster Target Tracking Based on the DBSCAN Clustering Algorithm and Labeled RFS
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