Ni Peishuang, Miao Chen, Tang Hui, Jiang Mengjie, Wu Wen
Ministerial Key Laboratory of JGMT, Nanjing University of Science and Technology, Xiao Ling Wei200#, Nanjing 210094, China.
Sensors (Basel). 2020 Apr 18;20(8):2316. doi: 10.3390/s20082316.
Foreign object debris (FOD) detection can be considered a kind of classification that distinguishes the measured signal as either containing FOD targets or only corresponding to ground clutter. In this paper, we propose a support vector domain description (SVDD) classifier with the particle swarm optimization (PSO) algorithm for FOD detection. The echo features of FOD and ground clutter received by the millimeter-wave radar are first extracted in the power spectrum domain as input eigenvectors of the classifier, followed with the parameters optimized by the PSO algorithm, and lastly, a PSO-SVDD classifier is established. However, since only ground clutter samples are utilized to train the SVDD classifier, overfitting inevitably occurs. Thus, a small number of samples with FOD are added in the training stage to further construct a PSO-NSVDD (NSVDD: SVDD with negative examples) classifier to achieve better classification performance. Experimental results based on measured data showed that the proposed methods could not only achieve a good detection performance but also significantly reduce the false alarm rate.
异物碎片（FOD）检测可被视为一种分类，即将测量信号区分为包含FOD目标或仅对应于地面杂波。在本文中，我们提出了一种结合粒子群优化（PSO）算法的支持向量域描述（SVDD）分类器用于FOD检测。首先在功率谱域中提取毫米波雷达接收到的FOD和地面杂波的回波特征，作为分类器的输入特征向量，接着通过PSO算法对参数进行优化，最后建立PSO-SVDD分类器。然而，由于仅利用地面杂波样本训练SVDD分类器，不可避免地会出现过拟合。因此，在训练阶段添加少量带有FOD的样本，进一步构建PSO-NSVDD（NSVDD：带有负样本的SVDD）分类器以实现更好的分类性能。基于实测数据的实验结果表明，所提出的方法不仅能够实现良好的检测性能，还能显著降低误报率。
