A signal fingerprint feature extraction method based on decomposition and fusion for radar emitter individual identification

Abstract

Radar emitter individual identification is one of the key technologies of modern electronic countermeasure reconnaissance and electronic intelligence. With the advancement of radar technology and the increasingly complex electromagnetic environment, existing methods for identifying emitter are gradually becoming unable to meet the performance requirements of modern radar individual identification. Aiming at improving the adaptability of feature extraction for non-cooperative radar emitter signals and the robustness of individual identification in the complex modern electronic warfare environment, a signal fingerprint feature extraction method based on decomposition and fusion is proposed. It firstly integrates signal decomposition and scattering convolution networks (SCN) to adaptively extract the multi-scale intra-pulse feature of the signal, while removing the potential noise of the redundant component by energy proportion. And then a deep feature fusion model based on multi-head self-attention and residual connection is proposed to fuse the multi-scale features and the time domain features to further extract signal fingerprint of radar emitter. Experimental results based on the real radar emitter signals demonstrate that the identification method proposed in this paper can more effectively extract signal fingerprint features and the identification accuracy reaches 96.45%, which outperforms other existing identification methods.