Asynchronous and non-stationary interference mitigation method

被引：0
|
作者：
Deng W. [1 ]
Huang Z. [1 ,2 ]
Wang X. [1 ]
Dai D. [3 ]
Chen L. [3 ]
机构：
[1] College of Electronic Science and Technology, National University of Defense Technology, Changsha
[2] College of Electronic Engineering, National University of Defense Technology, Hefei
[3] PLA Unit 95438, Meishan
来源：
Guofang Keji Daxue Xuebao/Journal of National University of Defense Technology | 2023年 / 45卷 / 05期
关键词：
asynchronous; deep learning; interference mitigation; non-stationary; sparse component analysis;
D O I：
10.11887/j.cn.202305003
中图分类号：
学科分类号：
摘要：
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. © 2023 National University of Defense Technology. All rights reserved.
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页码：21 / 29
页数：8
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