A Hierarchical Foreign Object Debris Detection Method Using Millimeter Wave Radar

Baoshuai WANG; Zhu LAN; Zhengjie LI; Xiaobin WANG; Hongtao HU

doi:10.11999/JEIT180200

Volume 40 Issue 11

Oct. 2018

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Baoshuai WANG, Zhu LAN, Zhengjie LI, Xiaobin WANG, Hongtao HU. A Hierarchical Foreign Object Debris Detection Method Using Millimeter Wave Radar[J]. Journal of Electronics & Information Technology, 2018, 40(11): 2676-2683. doi: 10.11999/JEIT180200

Citation:

Baoshuai WANG, Zhu LAN, Zhengjie LI, Xiaobin WANG, Hongtao HU. A Hierarchical Foreign Object Debris Detection Method Using Millimeter Wave Radar[J]. Journal of Electronics & Information Technology, 2018, 40(11): 2676-2683. doi: 10.11999/JEIT180200

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A Hierarchical Foreign Object Debris Detection Method Using Millimeter Wave Radar

doi: 10.11999/JEIT180200

Science and Technology on Electronic Information Control Laboratory, Chengdu 610036, China

Received Date: 2018-02-28
Rev Recd Date: 2018-08-13

Available Online: 2018-08-22

Publish Date: 2018-11-01

Abstract

Abstract

Detection of stationary little targets in heavy ground clutter is the key problem facing the millimeter wave airport runway Foreign Object Debris (FOD) detection radar. This paper proposes a hierarchical FOD detection algorithm based on power spectrum feature extraction and Support Vector Domain Description (SVDD) classifier. The clutter map Constant False Alarm Rate (CFAR) detection algorithm is first utilized to suppress the complex background clutter. In order to solve the high false alarm problem after the clutter suppression, the power spectrum features are extracted to transform the radar returns into the feature domain where the FOD and false alarm are more distinguishable. Finally, the one-class SVDD classifier is utilized to categorize the FOD and false alarm into different kinds so as to reduce the false alarm rate. Experimental results based on measured data show that the proposed method can achieve good detection performance.
- Millimeter wave radar,
- Foreign Object Debris (FOD) detection,
- Power spectrum features,
- One-class classifier

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References(19)

References

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