A robust multiple target tracking framework with Transformer-based data association and dynamic trajectory management in challenging environments

In this paper, a robust and reliable solution is proposed for the multiple target tracking (MTT) problem with position-only measurements in challenging environments involving unknown target numbers, high clutter, and frequent missed detections. Limited positional measurements complicate the disambiguation of closely spaced trajectories, and are further exacerbated by high false alarm and missed detection rates. To address these issues, a learning-based data association method embedded in a tracking framework with a dynamic score-based trajectory management strategy is presented. Specifically, a Cross Transformer-based Data Association (CTDA) method is proposed, which leverages cross architecture and Transformer mechanisms to effectively extract discriminative features from position measurements. Furthermore, a multiple target tracking framework is presented which integrates three key components: a Best Linear Unbiased Estimation (BLUE) filter that maintains individual trajectories while providing optimal state estimates, a dynamic trajectory scoring mechanism that continuously updates trajectory confidence based on historical association performance provided by the association network, and a trajectory management strategy that handles the full life-cycle management of trajectories, including the initialization, maintenance, and termination of the complete trajectory. Comprehensive simulation results demonstrate that the proposed method outperforms conventional algorithms in terms of tracking accuracy, reliability, and computational efficiency under complex conditions, including an over 50 % increase in data association performance and a substantial 58 % reduction in computational time.