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Enhanced multi-model multi-scan data association and tracking algorithm via convex variational inference ☆
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AI
DOI：10.1016/j.sigpro.2024.109520.png)
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Abstract
En 中文
Tracking multiple targets with unknown measurement -to -target association and uncertain target dynamics is a significant problem that arises in various applications such as surveillance monitoring and intelligent transportation systems. In this paper, we propose an enhanced multi -model multi -scan data association algorithm to address the problem of tracking multiple maneuvering targets. First, we use a probabilistic graphical model to represent the joint distribution of the dynamic model indices, target state, and multi -scan data association variables. This formulation transforms the inference of marginal distributions into a Bethe free energy (BFE) problem. Next, to transform the BFE problem into a convex one, we demonstrate that the BFE function can be made convex through re -weighting. Additionally, we decompose the re -weighted BFE function into a block -wise sum form. We prove that under certain regularization conditions, each block of the reweighted BFE is convex, ensuring convergence of the primal-dual coordinate ascent algorithm to the minimum of the overall re -weighted BFE. Finally, we provide a particle implementation of the proposed algorithm, accompanied by an analysis of its complexity. Simulation results indicate that the proposed algorithm exhibits favorable performance when compared to both the single -model multi -scan algorithm and the multi -model single -scan algorithm.
Keywords:
Maneuvering target tracking
Multi-scan data association
Variational inference
Primal-dual coordinate ascent