Electronic communication signals are susceptible to noise and interference in complex electromagnetic environments, resulting in a decrease in feature extraction accuracy. This paper proposes a cyclic frequency feature extraction algorithm that integrates wavelet noise reduction and LCL-FRESH filter. The signal quality is optimized by the adaptive wavelet threshold noise reduction algorithm, and the wavelet coefficients of the characteristic waveforms are screened by combining the fluctuation statistics method. The separation of time-frequency overlapped signals is realized by using LCL-FRESH filter, and the feature extraction ability is enhanced by combining the cyclic cumulative volume reconstruction technique. The Matlab test platform is selected for simulation experiments, and the curve obtained by extreme value threshold noise reduction has more burrs, and the overall is obviously not as smooth as the curve after wavelet threshold noise reduction. The SNR advantage of the LCLFRESH algorithm for the same reconstruction error is about 2dB. The accuracy of the anti-jamming cyclic frequency extraction algorithms for electronic communication signals are all above 92%, and the average completeness of the extraction results reaches 94.2%.
