Dual-Channel BiGRU–Transformer-Graph Fusion Network for Space Micromotion Targets Recognition Based on Radar Network Systems

The radar network system can provide multi-band and multi-view target information, which helps improve target recognition ability. A space micro-motion targets recognition method based on radar network systems (RNS) with a dual-channel bidirectional gated recurrent unit (BiGRU)-Transformer-graph fusion (DC-BiGT-GF) network is proposed in this paper. Firstly, a temporal feature extraction subnetwork based on BiGRU-Transformer is utilized to process the real and imaginary parts of complex-valued radar cross section (RCS) in parallel to capture the local and global temporal dependencies. Secondly, a spatial feature extraction subnetwork is designed to extract the potential spatial dependencies, which integrates a predefined graph and an adaptive graph. In the real part channel, the Euclidian distance between radars is used to construct the adjacency matrix to represent the predefined graph structure, and the adaptive adjacency matrix is designed to learn the potential graph structure from end to end. To represent the frequency domain features, the phase difference is applied in the imaginary part channel to build a predefined adjacency matrix. Meanwhile, the adaptive adjacency matrix is calculated using cosine similarity to obtain the geometric features. Finally, extensive experiments show that the DC-BiGT-GF network can reliably recognize the space micro-motion targets under low SNR and low radar pulse repetition frequency conditions. Recognition accuracy is greatly improved compared to the baseline methods.