Multistation Cooperative Radar Target Recognition Based on an Angle-guided Transformer Fusion Network

Abstract

Multistation cooperative radar target recognition aims to enhance recognition performance by utilizing the complementarity between multistation information. Conventional multistation cooperative target recognition methods do not explicitly consider the issue of interstation data differences and typically adopt relatively simple fusion strategies, which makes it difficult to obtain accurate and robust recognition performance. In this study, we propose an angle-guided transformer fusion network for multistation radar High-Resolution Range Profile (HRRP) target recognition. The extraction of the local and global features of the single-station HRRP is conducted via feature extraction, which employs a transformer as its main structure. Furthermore, three new auxiliary modules are created to facilitate fusion learning: the angle-guided module, the prefeature interaction module, and the deep attention feature fusion module. First, the angle guidance module enhances the robustness and consistency of features via modeling data differences between multiple stations and reinforces individual features associated with the observation perspective. Second, the fusion approach is optimized, and the multilevel hierarchical fusion of multistation features is achieved by combining the prefeature interaction module and the deep attention feature fusion module. Finally, the experiments are conducted on the basis of the simulated multistation scenarios with measured data, and the outcomes demonstrate that our approach can effectively enhance the performance of target recognition in multistation coordination.

Keywords

• multistation cooperative radar target recognition
• high-resolution range profile (hrrp)
• angle-guided
• attention feature fusion
• transformer fusion network