A Hierarchical Foreign Object Debris Detection Method Using Millimeter Wave Radar
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摘要: 强杂波背景下的弱小静止目标检测是毫米波机场跑道异物(FOD)检测雷达面临的核心问题。该文提出一种基于功率谱特征和支持向量域描述(SVDD)一类分类器的FOD分层检测算法。该算法首先利用杂波图恒虚警率(CFAR)检测器对复杂背景杂波进行杂波对消处理，针对对消后虚警过多的问题，对对消后的数据提取功率谱特征，将其转换到特征域，最后利用SVDD一类分类器在特征域实现对FOD和虚警的分类。基于实测数据的试验结果表明所提方法可以获得较好的检测性能。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.
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