DRFM-BASED REPEATER JAMMING COGNITION METHOD BASED ON RESNET WITH CHANNEL-ATTENTION MECHANISM

With the development of electronic countermeasure (ECM) technology, various kinds of digital radio frequency memory (DRFM)-based repeater jamming have been proposed, resulting in great influence on radar detection, localization and tracking. It is hard to obtain sufficient jamming samples, which brings difficulties to the jamming cognition. To solve this issue, a DRFM-based repeater jamming cognition method based on ResNet with channel-attention mechanism is proposed. In the proposed method, short-time Fourier transform (STFT) is utilized to generate the time-frequency maps of DRFM-based repeater jamming. Then, the 2D-wavelet transform is used to decompose the time-frequency maps and extract the texture feature. Moreover, the ResNet blocks with channel-attention mechanism are utilized to enhance the non-linear fitting ability of the network under small sample learning situation. Several simulations are conducted to illustrate that the proposed method achieves a higher classification accuracy for DRFM-based repeater jamming.