A feature extraction and recognition method for interrupted sampling repeater jamming

Abstract

In order to achieve anti-jamming, radar needs to identify the jamming signals to distinguish between the target echo signals and the jamming signals. Interrupted sampling repeater jamming (ISRJ) is a widely used jamming type in radar and electronic countermeasures. In this paper, we propose an efficient channel attention (ECA)-based one-dimensional residual neural network method for the ISRJ signal recognition. The joint features based on axially integral bispectrum and square integral bispectrum are used to extract the target echo signal and ISRJ signal. The ECA-based one-dimensional residual neural network is applied to the ISRJ recognition. The experiment results show that the proposed ECA-based one-dimensional residual neural network has better correct recognition rate under low signal-to-noise ratio and can offer 30.17% reduction in timeliness than the improved convolutional neural network-residual network (CNN-ResNet) method.