Space Target Recognition Based on Radar Network Systems With BiGRU-Transformer and Dual Graph Fusion Network

Abstract

Heterogeneous radar network systems can provide multiband and multiangle information about targets, enhancing the ability to recognize space targets. This article proposes a space target recognition method based on a bidirectional gated recurrent unit (BiGRU)-Transformer and dual graph fusion (BiGT-DGF) network. Through a temporal information extraction subnetwork, the BiGRU and Transformer are used to dynamically model a radar cross section (RCS) time series under multiple bands and angles, effectively exploiting both the local and global temporal dependencies. Through a spatial information extraction subnetwork, which integrates predefined graphs with self-adaptive graphs, the spatial dependencies between various radars are dynamically and adaptively captured. On this basis, the prediction output layer utilizes the spatiotemporal information extracted by the above two subnetworks to effectively recognize space targets. The experimental results show that the proposed method can reliably recognize space targets even under low signal-to-noise ratios (SNRs) and low pulse repetition frequencies.