基于深度神经网络的收发同时系统中自干扰数字对消算法

为了解决转发式干扰机收发同时系统中的自干扰难以对消问题，该文设计一种基于深度神经网络(DNN)的自干扰对消算法。在自干扰信号与目标信号强相关且混叠的情况下该算法可以有效地消除自干扰信号。在此基础上，该文利用分段侦收的信号快速生成干扰的方法，验证了该算法在收发同时系统中自干扰信号对消的可行性，实现了基于本脉冲的雷达干扰信号构建，对敌方雷达快速做出反应，在电子对抗中占据有利地位。该文利用典型的线性调频(LFM)和二进制相移键控(BPSK)雷达信号生成数据集训练深度神经网络，用测试集去测试网络输出的模型。实验结果表明:在收发同时系统中目标信号与自干扰信号混叠的情况下，基于DNN的自干扰对消算法可以有效消除自干扰信号，在信干比–8 dB的情况下，对消比可达到26 dB以上。

Self-interference Digital Cancellation Algorithm in Simultaneous Transceiver System Based on Deep Neural Network

In order to solve the problem that the self-interference is difficult to eliminate in the transceiver system of the repeater jammer, a self-interference cancellation algorithm based on Deep Neural Network(DNN) is presented in this paper. This proposed algorithm can effectively eliminate the self-interference signal when the self-interference signal is correlated and mixed with the target signal. On this basis, the interference generation method of segmenting interception is used in this paper, which verifies the feasibility of jamming signal cancellation in the simultaneous transceiver system. It realizes the construction of radar jamming signal considering the pulse, which can quickly respond to enemy radars and occupy a favorable position in electronic countermeasures. In this paper, a typical Linear Frequency Modulation(LFM) and Binary Phase Shift Keying (BPSK) radar signal are used to generate a data set to train the DNN network, and the test set is used to test the output model of the network. The experimental results show that the self-interference cancellation algorithm based on DNN can effectively eliminate the self-interference signal when the target signal and self-interference signal are mixed in the simultaneous transceiver system, and the cancellation ratio can reach more than 26 dB when the signal to interference ratio is –8 dB.