Dual-Channel BiGRU–Transformer-Graph Fusion Network for Space Micromotion Targets Recognition Based on Radar Network Systems
Year: 2025
Type: article
Abstract: 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.
Institution: Air Force Engineering University
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,645
Awards: G3937783750, G5686462201
OpenAlex ID: W4409128956