JDTFormer: A Hybrid-Scale Spatio-Temporal Transformer Network for Automotive Radar Extended Target Joint Detection and Tracking

Due to the increase of signal bandwidth, automotive radar can receive multiple scatting centers from the target of interest. This is typically described by an extended target model. However, existing methods for extended target detection and tracking often exhibit subpar performance because they struggle to adapt to the hybrid-scale and time-varying texture feature exhibited by moving traffic participants. Therefore, this work presents a unified framework hybrid-scale spatio-temporal transformer for joint detection and tracking of hybrid-scale extended targets. It consists of three progressive stages: 1) hybrid-scale embedding network (HEN); 2) spatio-temporal transformer; and 3) Wasserstein loss. The HEN extracts the features of hybrid-scale targets by parallel multiple convolution kernels of different scales. Then, the spatio-temporal transformer locates targets by integrating these spatial-temporal features across the time. It can allow the target’s hybrid-scale and time-varying texture feature changes incurred through the movement, while eliminating the need for cross frame target association, thus achieving robust detection and tracking. The Wasserstein loss metric is designed to assist the model in optimizing hybrid-scale target location proficiency, especially for small targets. With intensive experiments on simulated datasets and public real datasets, such as RAMP-convolutional neural network and RADDet, the proposed model achieves better detection and tracking performance than existing methods with different noise added, and shows strong robustness in various traffic scenarios.