| 引用本文: | 李春腾,蒋宇中,刘芳君,等.干扰抑制类生成式对抗网络.[J].国防科技大学学报,2020,42(5):1-8.[点击复制] |
| LI Chunteng,JIANG Yuzhong,LIU Fangjun,et al.Interference suppression generative adversarial nets[J].Journal of National University of Defense Technology,2020,42(5):1-8[点击复制] |
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| 干扰抑制类生成式对抗网络 |
| 李春腾1,2,蒋宇中2,刘芳君3,贾书阳2,李松林4 |
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(1. 中国人民解放军92330.部队, 山东 青岛 266000;2. 海军工程大学 电子工程学院, 湖北 武汉 430033;3. 云南民族大学 数学与计算机科学学院, 云南 昆明 650500;4. 海军工程大学 电气工程学院, 湖北 武汉 430033)
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| 摘要: |
| 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。 |
| 关键词: 超低频通信 生成式对抗网络 干扰抑制 磁性天线 广义旁瓣抵消 阻塞矩阵 |
| DOI：10.11887/j.cn.202005001 |
| 投稿日期：2019-03-07 |
| 基金项目:国家自然科学基金资助项目(41631072) |
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| Interference suppression generative adversarial nets |
| LI Chunteng1,2, JIANG Yuzhong2, LIU Fangjun3, JIA Shuyang2, LI Songlin4 |
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(1. The PLA Unit 92330, Qingdao 266000, China;2. College of Electronic Engineering, Naval University of Engineering, Wuhan 430033, China;3. Academy of Mathematics and Computer Science, Yunnan Nationalities University, Kunming 650500, China;4. College of Electrical Engineering, Naval University of Engineering, Wuhan 430033, China)
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| Abstract: |
| 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. |
| Keywords: extremely-low-frequency communication generative adversarial nets interference suppression magnetic antenna generalized sidelobe cancellation blocking matrix |
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