ArticleSensors (Basel, Switzerland)2021
A FOD Detection Approach on Millimeter-Wave Radar Sensors Based on Optimal VMD and SVDD.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- FOD detection research using BSM-YOLO during construction without air service suspension.Scientific reports · 2025Article
- An Anti-Mainlobe Suppression Jamming Method Based on Improved Blind Source Separation Using Variational Mode Decomposition and Wavelet Packet Decomposition.Sensors (Basel, Switzerland) · 2025Article
- Foreign Object Debris Detection for Optical Imaging Sensors Based on Random Forest.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
5 authors.
Funding
Abstract
Foreign object debris (FOD) on airport runways can cause serious accidents and huge economic losses. FOD detection systems based on millimeter-wave (MMW) radar sensors have the advantages of higher range resolution and lower power consumption. However, it is difficult for traditional FOD detection methods to detect and distinguish weak signals of targets from strong ground clutter. To solve this problem, this paper proposes a new FOD detection approach based on optimized variational mode decomposition (VMD) and support vector data description (SVDD). This approach utilizes SVDD as a classifier to distinguish FOD signals from clutter signals. More importantly, the VMD optimized by whale optimization algorithm (WOA) is used to improve the accuracy and stability of the classifier. The results from both the simulation and field case show the excellent FOD detection performance of the proposed VMD-SVDD method.
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Registered trials
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