Semantic Feature-Enhanced Graph ATtention Network for Radar Target Recognition in Heterogeneous Radar Network

Abstract

Radar target recognition (RTR), as a key technique of intelligent radar systems, has been widely investigated. Accurate RTR at low signal-to-noise ratios (SNRs) still remains an open challenge. Considering that most existing methods are based on a single radar or the homogeneous radar network, we extend RTR to the heterogeneous radar network to improve the robustness of RTR, which uses the radar cross Section (RCS) signals at low SNRs by further exploiting the frequency-domain information. In this article, a Semantic Feature-Enhanced Graph ATtention Network (SFE-GAT) is proposed, which extracts semantic features from both the source and transform domains via the long short-term memory (LSTM) and GAT layers, then fuses them in the semantic space using an attention mechanism, and further distills higher-level semantic features using a GAT layer before classification. Extensive experiments are carried out to validate that the proposed SFE-GAT model can greatly improve the RTR accuracy in the low SNR region.