Name
Title | ||
|---|---|---|
Small Foreign Object Debris Detection for Millimeter-Wave Radar Based on Power Spectrum Features. |
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/s20082316 | SENSORS |
Keywords | DocType | Volume |
FOD detection,feature extraction,millimeter-wave radar,the PSO algorithm,SVDD classifier | Journal | 20.0 |
Issue | ISSN | Citations |
8.0 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peishuang Ni | 1 | 0 | 0.34 |
| Chen Miao | 2 | 0 | 0.34 |
| Hui Tang | 3 | 0 | 0.34 |
| Mengjie Jiang | 4 | 0 | 0.34 |
| Wen Wu | 5 | 517 | 47.40 |