An anti‐jamming method in multistatic radar system based on convolutional neural network - Jieyi Liu; Maoguo Gong; Mingyang Zhang; Hao Li; Shanshan Zhao (IET Signal Processing)
Abstract For the existing jamming discrimination methods on the multistatic radar system, the single feature of target echo space correlation is utilised as the metric, which leads to the lack of comprehensive feature extraction and universal discrimination algorithm. In this study, a discrimination method in a multistatic radar system based on the convolutional neural network is proposed. This proposal combines the advantages of multiple‐radar systems cooperative detection technology with the convolutional neural network, and effectively applies to the field of anti‐deception jamming, which takes full advantage of unknown information of echo data to obtain multi‐dimensional, comprehensive, complete and deep feature differences besides correlation, so as to achieve a better jamming discrimination effect. The simulation results show that the proposed method can extract the multidimensional and separable essential features of echoes, and all these features have a strong degree of differentiation between targets and jamming, which effectively reduce the influence of noise and pulse number. At the same time, the influence of radar distribution on jamming discrimination under non‐ideal conditions is relieved, when the correlation coefficient of the true target reaches 0.4, the discrimination probability remains above 85%, which broadens the boundary conditions of the application process.