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 xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> -GLMB filter is proposed. In the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <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.
Highlights
With the continuous improvement of cluster control technology as well as unmanned autonomous technology, there is a pressing need for accurate, timely, and efficient ways of tracking cluster targets
We model the noise as random numbers that obey Gaussian distribution
In this paper, we use stochastic differential equations to model the cooperative interaction of the cluster and derive the state equation of the cluster targets
Summary
With the continuous improvement of cluster control technology as well as unmanned autonomous technology, there is a pressing need for accurate, timely, and efficient ways of tracking cluster targets. We combine the DBSCAN clustering algorithm with the δ-GLMB filter to achieve target tracking and cluster state estimation at the same time. The solution of Qct has the same form as Fct. Under the premise that the randomness in motion does not significantly affect the cooperative interaction, the noise covariance matrix of the ith target is Qsct. A DBSCAN clustering algorithm based on the distance of target motion similarity is proposed We apply this algorithm to the prediction step of the δ-GLMB filtering algorithm to achieve target and cluster state estimation at the same time. Reference [5] gives a detailed algorithm flow
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