| 引用本文: | 邓文,黄知涛,王翔,等.异步非平稳干扰抑制方法.[J].国防科技大学学报,2023,45(5):21-29.[点击复制] |
| DENG Wen,HUANG Zhitao,WANG Xiang,et al.Asynchronous and non-stationary interference mitigation method[J].Journal of National University of Defense Technology,2023,45(5):21-29[点击复制] |
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| 异步非平稳干扰抑制方法 |
| 邓文1,黄知涛1,2,王翔1,戴定川3,陈梁栋3 |
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(1. 国防科技大学 电子科学学院, 湖南 长沙 410073;2. 国防科技大学 电子对抗学院, 安徽 合肥 230037;3. 中国人民解放军95438部队, 四川 眉山 620860)
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| 摘要: |
| 为解决单通道条件下异步非平稳干扰抑制问题,提出基于数据驱动的稀疏分量分析干扰抑制方法,旨在从接收到的混叠信号中恢复期望信号。该方法利用深度卷积神经网络对输入/输出端数据间的复杂映射关系的强大建模能力,实现了目标信号稀疏域的自适应选择、稀疏域中目标信号稀疏表示的自适应学习以及目标信号的自动恢复。与以往干扰抑制算法不同,所提方法在时域上完成了“端到端”的信号波形恢复,且对混叠观测无先验要求,相比现有方法更具普适性。仿真实验验证了所提干扰抑制方法在不同环境噪声和干扰信号强度及泛化测试条件下的有效性,对干扰的抑制性能显著优于现有算法。 |
| 关键词: 干扰抑制 异步 非平稳 稀疏分量分析 深度学习 |
| DOI：10.11887/j.cn.202305003 |
| 投稿日期：2022-10-14 |
| 基金项目:国家自然科学基金面上资助项目(62271494) |
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| Asynchronous and non-stationary interference mitigation method |
| DENG Wen1, HUANG Zhitao1,2, WANG Xiang1, DAI Dingchuan3, CHEN Liangdong3 |
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(1. College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073,China;2. College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China;3. The PLA Unit 95438,Meishan 620860, China)
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| Abstract: |
| To address the problem of mitigating asynchronous non-stationary interference in single-channel conditions, a data-driven sparse component analysis method was proposed. The aim of this method is to recover the desired signal from the received mixed signals. This method used the powerful modeling ability of deep convolutional neural network to model the complex mapping between the input and output data, and realized the adaptive selection of sparse domain of target signals, the adaptive learning of sparse representation of target signals in sparse domain, and the automatic recovery of target signals. Unlike the previous interference mitigation algorithms, the proposed method completed the “end-to-end” signal waveform recovery in the time domain, and had no prior requirement for aliasing observation, which was more universal than the existing methods. Simulation experiments verified the effectiveness of the proposed interference mitigation method under different environmental noise and interference signal strength and generalization test conditions, and the interference mitigation performance is significantly better than the existing algorithms. |
| Keywords: interference mitigation asynchronous non-stationary sparse component analysis deep learning |
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