Intelligent data association and tracking of different types of multiple targets based on Transformer

Considering tracking different types of multiple targets, the current tracking system use fixed model parameters, which fail to fully exploit motion characteristics from different types of targets. This limitation can lead to decreased tracking accuracy and even track loss or mis-tracking. To address this issue, a Transformer-based intelligent data association and tracking method is proposed for different types of multiple targets. In the case of time sliding window, by integrating sparse attention and self-attention mechanisms, the target-type features and track association features are deeply explored and exploited from the historical radar data and short-time track data, to adaptively adjust the process noise covariance matrix. This is beneficial to realize the refined motion modeling of different types of targets and acquire the reliable association between every radar echo and each target track, thereby producing high-quality tracking outputs for different types of multiple targets. Simulation results in the typical scenario of tracking different types of multiple targets with varying clutter densities demonstrate that, compared with the joint probabilistic data association filter, multiple hypothesis tracking, probability hypothesis density filter, and intelligent data association tracking with fixed model parameters, the proposed method achieves superior tracking accuracy and stability.