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Small Foreign Object Debris Detection for Millimeter-Wave Radar Based on Power Spectrum Features

机译：基于功率谱特征的毫米波雷达小异物检测

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摘要

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.
机译：可以将异物碎片（FOD）检测视为一种分类，该分类将测量的信号区分为包含FOD目标或仅对应于地面杂波。在本文中，我们提出了一种支持向量域描述（SVDD）分类器，并使用粒子群优化（PSO）算法进行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，然后通过PSO算法优化参数，最后建立PSO-SVDD分类器。但是，由于仅利用地面杂波样本来训练SVDD分类器，因此不可避免地会发生过度拟合。因此，在训练阶段添加了少量带有FOD的样本，以进一步构建PSO-NSVDD（NSVDD：带有负示例的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，所提方法不仅可以达到良好的检测性能，而且可以大大降低误报率。

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