卷积线性混合模型下的复非高斯信号盲源提取

李苗苗; 吕晓德; 王宁; 刘忠胜

doi:10.13700/j.bh.1001-5965.2021.0197

卷积线性混合模型下的复非高斯信号盲源提取

doi: 10.13700/j.bh.1001-5965.2021.0197

李苗苗^{1, 2, 3},
吕晓德^{1, 2, ,},
王宁^{1, 2, 3},
刘忠胜^{1, 2}

1.
中国科学院空天信息创新研究院，北京 100094
2.
微波成像技术国家级重点实验室，北京 100190
3.
中国科学院大学，北京 100049

详细信息

通讯作者:
E-mail：lvxd@aircas.ac.cn

中图分类号: TN973；TN958
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- 被引次数: 0
出版历程
- 收稿日期: 2021-04-15
- 录用日期: 2021-07-04
- 网络出版日期: 2021-07-14
- 整期出版日期: 2023-01-30

Blind source extraction of complex non-Gaussian signals based on convolution linear mixture model

LI Miaomiao^{1, 2, 3},
LYU Xiaode^{1, 2
, ,},
WANG Ning^{1, 2, 3},
LIU Zhongsheng^{1, 2}

1.
Aerospace Information Research Institute，Chinese Academy of Sciences，Beijing 100094，China
2.
National Key Lab of Microwave Imaging Technology，Beijing 100190，China
3.
University of Chinese Academy of Sciences，Beijing 100049，China

More Information

Corresponding author: E-mail：lvxd@aircas.ac.cn

摘要

摘要:
雷达信号的多径效应导致基于瞬时线性混合模型的盲源分离算法不再适用。为此，提出了一种基于FastICA的复非高斯信号盲源提取方法。该方法将混合系统建模为卷积线性混合模型，使得信号模型中不需要将每个多径信号都看作一个独立的源信号，既节约了接收通道数量，又降低了盲源分离过程的复杂度，利用待提取信号的非高斯性实现高斯背景下复非高斯信源的提取。实验结果表明：在信干比为−30 dB时，所提方法能够快速、有效地处理卷积线性混合模型下复非高斯信源的提取问题，为该场景下的微弱信号提取提供了一种新的方法。
- 多径效应 /
- 卷积线性混合 /
- 复FastICA算法 /
- 非高斯信号 /
- 盲源提取
Abstract:
Due to the multipath effect of the radar signal, the blind source separation algorithm based on the instantaneous linear mixture model is no longer applicable. A blind source extraction method for complex non-Gaussian signals based on the FastICA algorithm is proposed. The mixed system is modeled as a convolutional linear mixture model, so that each multipath signal does not need to be regarded as an independent source signal in the signal model, which not only saves the number of receiving channels, but also reduces the complexity of blind source separation process. The non-Gaussian feature of the signal to be extracted is used to extract complex non-Gaussian sources in Gaussian background. The experimental results show that when the signal to interference ratio is −30 dB, the proposed method can quickly and effectively deal with the extraction of complex non-Gaussian sources in the convolutional linear mixture model, which provides a new method for weak signal extraction in this scene.
- multipath effect /
- convolution linear mixing /
- complex FastICA algorithm /
- non-Gaussian signal /
- blind source extraction

HTML全文

图 1 复非高斯信源提取模型

Figure 1. Complex non-Gaussian source extraction model

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图 2 单个线性调频信号与高斯干扰信号混合

Figure 2. Mixture of single linear frequency modulation signal and Gaussian interference signal

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图 3 瞬时和卷积线性混合模型分离效果对比

Figure 3. Comparison of separation effect between instantaneous and convolution linear mixture models

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图 4 瞬时和卷积线性混合模型分离相关性对比

Figure 4. Comparison of separation correlation between instantaneous and convolution linear mixture models

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图 5 多线性调频信号盲源提取

Figure 5. Blind source extraction of multiple linear frequency modulation signals

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图 6 雷达脉冲回波数据盲源提取

Figure 6. Blind source extraction of radar pulse echo data

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