A Radar Signal Recognition Method Based on Denoised Ambiguity Function and EfficientNet
JIANG Liangjian
XIE Weipeng
WU Lihua
Abstract：Radar signal recognition is the key technology of electronic countermeasures.In view of the ambiguity function's unique effect on characterizing signal inherent structure,and strong recognition performance combined with deep neural network,this paper proposes a recognition method based on contour lines of ambiguity function with Ensemble Empirical Mode Decomposi-tion(EEMD)noise reduction and EfficientNet.First,the appropriate EEMD parameters to denoise the time domain signals and con-tour lines'datasets are created.Then,a Deep Network based on EfficientNet-B0 model is established and transfer learning is used to complete the training of labeled data.Finally,the network is used to achieve radar emitter signals recognition.The simulated ex-periments show that the average recognition accuracy rate of six kinds of complex modulated signals,i.e.,BPSK,BFSK,FMCW,QPSK,LFM-BC and MSEQ,by proposed method keeps above 99.67%in fixed SNR environment above-10dB with good general-ization ability and strong robustness.
Keywords：signal recognitionambiguity functioncontour linesEEMDEfficientNet
Publication Date：2023-11-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 78-83 )