A robust detection method for micro-Doppler feature of rotating blades under low SNR

Congshuang Xie; Junjie Li; Qin Chen; Zihao Zhao; Chunyi Song; Zhiwei Xu

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A robust detection method for micro-Doppler feature of rotating blades under low SNR

机译：低信噪比下旋转叶片微多普勒特征的鲁棒检测方法

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Rotation of rotor blades is typically micro-motion and would induce micro-Doppler modulation. However, accurate detection will become extremely difficult when the echo signal is corrupted by strong noise. This paper proposes a method to realize extraction of micro-Doppler feature from the rotating rotor blade echo signals with low signal-to-noise ratio (SNR). The proposed method is characterized by applying low-rank decomposition for saliency detection of spectrogram. MATLAB simulation results verify that the proposed method is effective in saliency detection of the spectrogram from rotor blade echo signals and is robust in estimating the maximum micro-Doppler shift under low SNR.

机译：转子叶片的旋转通常是微运动，并且将引起微多普勒调制。但是，当回波信号被强噪声破坏时，精确检测将变得极为困难。本文提出了一种从低信噪比的旋转转子叶片回波信号中提取微多普勒特征的方法。该方法的特征在于将低秩分解应用于频谱图的显着性检测。 MATLAB仿真结果证明，该方法可以有效地从转子叶片回波信号中对频谱图进行显着性检测，并且在估计低SNR时可以最大地估计最大微多普勒频移。

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《IEICE Communications Express》 |2019年第8期|共6页
作者
Congshuang Xie; Junjie Li; Qin Chen; Zihao Zhao; Chunyi Song; Zhiwei Xu;
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中图分类通信;
关键词
spectrogrammicro-Dopplersaliency detectionHough transform;

机译：频谱图微多普勒显性检测霍夫变换;

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A robust detection method for micro-Doppler feature of rotating blades under low SNR

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