Abstract: In order to realize high-precision identification of multiple communication emitters in low signal-to-noise ratio (SNR) environment, a method of communication emitter identification based on steady-state cyclic spectrum characteristics is proposed. By using the strong robustness of cyclic spectrum's cross-sectional spectrum in frequency domain to Gaussian noise, the intrinsic differences between shaping filters of different emitters are extracted for identification. Specifically, the cyclic spectrum's cross-sectional spectra in frequency domain are extracted from the received steady-state signals, and the dimensions are reduced by principal component analysis. Then the emitters' categories are determined by Pearson correlation coefficient method, probabilistic neural network and Fréchet distance method, etc. Simulation shows that the proposed feature is superior to the traditional slice feature in cyclic frequency domain by using probabilistic neural network and Pearson correlation coefficient, which proves that it has certain application value.

Key words: communication emitter identification, steady-state cyclic spectrum feature, Pearson correlation coefficient method, probabilistic neural network, Fréchet distance method