干扰抑制类生成式对抗网络

为进一步改善超低频频段的通信质量,在传统改进广义旁瓣抵消算法的基础上,提出新的超低频干扰抑制算法——生成式旁瓣抵消算法。该算法将人工智能研究热点之一的生成式对抗网络模型引入广义旁瓣抵消算法中,通过优化设计生成模型的网络结构及相关超参数,有效地解决了原算法存在的期望信号残留问题,为旁瓣抵消通道中的后级滤波算法提供了与主通道相关性更强的干扰参考信息,从而提高了算法对主通道干扰估计的准确性。为了验证优化后生成模型的有效性以及所提算法对不同类别干扰的抑制能力,在实验室环境下搭建实验平台,设计了多组对照实验。实验结果表明:优化后的生成模型具有较好的生成能力、较好的鲁棒性以及相对较低的运算复杂度；相比于传统改进的广义旁瓣抵消算法,所提算法进一步提高了信号带宽内的信干噪比。

Interference suppression generative adversarial nets

In order to further improve the communication quality of the extremely-low-frequency communication further, based on the traditional improved generalized sidelobe cancellation, a new interference suppression algorithm in the field of extremely-low-frequency communication called generative sidelobe cancellation algorithm was proposed. Generative adversarial nets as one of the hot research topics in artificial intelligence was introduced into generalized sidelobe cancellation, the network structure and relevant hyperparameters of the generative model were designed and optimized, addressing the problem of the residual desired signal existing into the original algorithm effectively, providing more relevant reference information about the interference components in the main channel for the next-stage filtering algorithm of sidelobe cancellation channel, thereby enhancing the estimation accuracy of the interference components in the main channel. In order to validate the effectiveness of the optimized generative model and the suppression ability of the proposed algorithm on different types of interferences, an experimental platform was set up under the laboratory environment and multiple sets of controlled experiments were designed. The experimental results show that the optimized generative model has better generative ability, better robustness and relatively lower computational complexity. Compared with the traditional improved algorithm, the proposed algorithm can further improve the signal-to-interference-plus-noise ratio within the signal bandwidth further.