An anti‐jamming method in multistatic radar system based on convolutional neural network

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.

Consensus based target tracking against deception jamming in distributed radar networks

“…In signal-level fusion domain, the received signals from local radar sites are directly analysed and compared to suppress deception jamming. According to the spatial diversity of physical target [25], a series of approaches to combat deception jamming in radar network have been proposed [26][27][28][29][30][31]. The core idea is that the deception signals are only highly correlated with the difference in strength caused by antenna gain and path loss effect, so that the amplitude ratio from different radars is generally the same for all the false targets.…”

“…In ref. [28,31], clustering analysis approaches like convolutional neural network (CNN) have been introduced to discriminate false targets. Besides, in ref.…”

Jamming Effects on Hybrid Multistatic Radar Network Range and Velocity Estimation Errors

“…Recent works have looked to implement methods based on providing convolutional neural networks with multistatic radar data such that jamming discrimination can be carried out using features beyond signal correlation [12]. The proposed method was shown to be effective against deception jamming techniques.…”

“…The complex modulation of signals from distributed receivers are correlated to facilitate a data fusion method based on local measurements without information loss as typically seen in centralised decision making employed by multistatic networks. However, the proposed approaches in [12] and [13] are only shown to be implemented on single frequency multistatic systems and do not leverage the capability of frequency diversity which such a platform may have the potential to support.…”