基于特征谱特征的机场跑道异物分层检测算法

doi:10.11999/JEIT170178

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Abstract Detection of weak targets in heavy ground clutter is the key issue for Foreign Object Debris (FOD) surveillance radar on airport runways. A novel hierarchical FOD detection method is proposed based on eigenvalue spectrum feature extraction and Minimax Probability Machine (MPM). The clutter map Constant False Alarm Rate (CFAR) detection algorithm is utilized firstly to categorize radar echoes into two kinds, i.e., background clutter and the FOD returns (including the false alarm returns). Then eigenvalue spectrum features are extracted to transform the FOD returns and false alarm returns into the feature domain where the FOD and false alarm are more distinguishable. Finally, the MPM classifier is utilized to categorize the FOD and false alarm into different kinds so as to reduce the false alarm rate. Experiments results based on measured data show that the proposed method can achieve good detection performance.

Key words： Millimeter wave radar Airport runway Foreign Object Debris (FOD) Feature extraction Minimax Probability Machine (MPM)

Received: 01 March 2017 Published: 14 September 2017

PACS:

TN957.51

Corresponding Authors: WANG Baoshuai E-mail: wbsatr@163.com

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	WANG Baoshuai
	LIU Jianghong
	ZHENG Xiaoliang
	HE Minjue
	XIAO Qing

Cite this article:

WANG Baoshuai,LIU Jianghong,ZHENG Xiaoliang等. A Hierarchical FOD Detection Method Based on Eigenvalue Spectrum Features[J]. JEIT, 2017, 39(11): 2690-2696.

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http://jeit.ie.ac.cn/EN/10.11999/JEIT170178 OR http://jeit.ie.ac.cn/EN/Y2017/V39/I11/2690

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