A method of jamming recognition based on multi-domain joint convolutional neural network

Abstract

Radar intelligence has become the development trend of radar, and accurate jamming recognition plays a vital role in the intelligent perception of the radar. With the input of multi-domain signals, including time domain, pulse compression, and frequency domain signals, a multi-domain joint convolutional neural network model was developed to classify different types of radar jamming. The results in this paper showed that when the jamming-to-noise ratio of the jamming was greater than 2 dB, the recognition accuracy rate of this model was over 94%. Compared with the one-dimensional convolutional neural network model, the recognition rate of multi-domain joint model proposed here was greatly improved, which, to a certain extent, has promoted the development of radar intelligence.