Multi-faceted Graph Attention Network for Radar Target Recognition in Heterogeneous Radar Network

TL;DR

This paper introduces a Multi-faceted Graph Attention Network that enhances radar target recognition accuracy in heterogeneous radar networks, especially at low SNRs, by fusing features from multiple domains.

Contribution

It proposes a novel two-stream semantic feature fusion model using graph attention networks for improved low SNR radar target recognition.

Findings

Significantly improves recognition accuracy at low SNRs

Effective fusion of source and transform domain features

Outperforms existing methods in heterogeneous radar networks

Abstract

Radar target recognition (RTR), as a key technology of intelligent radar systems, has been well investigated. Accurate RTR at low signal-to-noise ratios (SNRs) still remains an open challenge. Most existing methods are based on a single radar or the homogeneous radar network, which do not fully exploit frequency-dimensional information. In this paper, a two-stream semantic feature fusion model, termed Multi-faceted Graph Attention Network (MF-GAT), is proposed to greatly improve the accuracy in the low SNR region of the heterogeneous radar network. By fusing the features extracted from the source domain and transform domain via a graph attention network model, the MF-GAT model distills higher-level semantic features before classification in a unified framework. Extensive experiments are presented to demonstrate that the proposed model can greatly improve the RTR performance at low SNRs.