数字预失真下的辐射源个体识别技术

With the development of communication radar technology, new techniques such as pre-distortion have emerged to address the non-linear effects of radiation source transmitters, which weaken the individual characteristics of dif⁃ ferent radiation sources and thus deteriorate the individual source identification performance. To address the problem of re⁃ duced individual source identification under pre-distortion, this paper proposes an individual source identification model based on the SincNet filter structure. This paper uses the Grad-CAM method to analyse the residual network-like activation region and extract the co-occurrence matrix features for radiation source identification to verify the effectiveness of the local features of the signal after pre-distortion. This paper then proposes a SincNet filter structure-based algorithm for individual source identification, which reduces the computational effort while providing higher identification accuracy at low signal-to-noise ratios. The negative effect of digital pre-distortion on the individual identification of radiation sources is verified ex⁃ perimentally and the results on the measured data show that the individual identification rate of the proposed method reach⁃ es 94% at a signal-to-noise ratio of 0 dB, which is a significant improvement compared to other advanced individual identifi⁃ cation algorithms in this paper.