基于特征谱特征的机场跑道异物分层检测算法

强杂波背景下的弱静止目标检测是机场跑道异物(Foreign Object Debris, FOD)监测雷达面临的关键问题。该文提出一种基于特征谱特征和最小最大概率机(Minimax Probability Machine, MPM)的FOD分层检测算法，首先利用杂波图恒虚警(Constant False Alarm Rate, CFAR)将雷达录取回波中的背景杂波和FOD回波(包含虚警)区分开，然后提取特征谱特征将在回波域中差异较小的FOD回波和虚警回波转换到区分性更大的特征域，最后利用MPM分类器实现对FOD和虚警的分类，从而达到降低虚警次数的目的。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。

A Hierarchical FOD Detection Method Based on Eigenvalue Spectrum Features

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.