1. Small Foreign Object Debris Detection for Millimeter-Wave Radar Based on Power Spectrum Features.
- Author
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Ni, Peishuang, Miao, Chen, Tang, Hui, Jiang, Mengjie, and Wu, Wen
- Subjects
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FOREIGN bodies , *POWER spectra , *BISTATIC radar , *RADAR , *PARTICLE swarm optimization , *FALSE alarms - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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