Enhanced multi-model multi-scan data association and tracking algorithm via convex variational inference

Abstract

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 re-weighted 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.