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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Bibliographic Details
Published in:IEEE access Vol. 9; pp. 43364 - 43377
Main Authors: Xue, Xirui, Huang, Shucai, Xie, Jiahao, Ma, Jiashun, Li, Ning
Format: Journal Article
Language:English
Published: Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3066629