Clutter Suppression Method for Short Range Slow Moving Target Detection

Lin ZHENG; Weiwei YAO; Chao YANG; Hongbing QIU

doi:10.11999/JEIT180031

Volume 40 Issue 10

Sep. 2018

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Lin ZHENG, Weiwei YAO, Chao YANG, Hongbing QIU. Clutter Suppression Method for Short Range Slow Moving Target Detection[J]. Journal of Electronics & Information Technology, 2018, 40(10): 2506-2512. doi: 10.11999/JEIT180031

Citation:

Lin ZHENG, Weiwei YAO, Chao YANG, Hongbing QIU. Clutter Suppression Method for Short Range Slow Moving Target Detection[J]. Journal of Electronics & Information Technology, 2018, 40(10): 2506-2512. doi: 10.11999/JEIT180031

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Clutter Suppression Method for Short Range Slow Moving Target Detection

doi: 10.11999/JEIT180031

1.
Guangxi Key Laboratory of Wireless Communication & Signal Processing, Guilin University of Electronic Technology, Guilin 541004, China
2.
Science & Technology on Information Transmission & Dissemination in Communication Networks Laboratory, Shijiazhuang 050081, China

Funds: The National Natural Science Foundations of China (61571143, 61371107), The Wideband Wireless Communications & Signal Processing Key Lab oratory. Foundation of Guangxi (GXKL061501), The Science and Technology on Communication Networks Laboratory Foundation (KX172600033)

Received Date: 2018-01-09
Rev Recd Date: 2018-05-02

Available Online: 2018-07-30

Publish Date: 2018-10-01

Abstract

Abstract

This paper proposes a method of clutter suppression based on phase encoding and subspace projection for close slow-moving target detection in strong clutter environment. In the framework, the periodic detection signal is modulated with phase encoding, and the clutter is whitened through echo decoding of the slow-time dimension to reduce the correlation between clutter and target echo. Furthermore interference subspace is constructed on the basis of the autocorrelation differences between whitened clutter and useful signal components. The receiving signal is projected to the signal subspace orthogonal to the clutter subspace for clutter suppression. Since the construction of clutter space does not need to assume the clutter model, it avoids the problem of mismatch between the model hypothesis and the actual environment. Simulation results and real data processing results show that this method has better performance than conventional methods under low signal-to-clutter ratio.
- Clutter suppression,
- Slow-moving target,
- Phase encoding,
- Subspace projection

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References(16)

References

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