A three-step classification framework to handle complex data distribution for radar UAV detection
Unmanned aerial vehicles (UAVs) have been used in a wide range of applications and become an increasingly important radar target. To better model radar data and to tackle the curse of dimensionality, a three-step classification framework is proposed for UAV detection. First we propose to utilize the...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier Ltd
2020
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| Online Access: | https://eprints.nottingham.ac.uk/63878/ |
| _version_ | 1848800068886331392 |
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| author | Ren, Jianfeng Jiang, Xudong |
| author_facet | Ren, Jianfeng Jiang, Xudong |
| author_sort | Ren, Jianfeng |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Unmanned aerial vehicles (UAVs) have been used in a wide range of applications and become an increasingly important radar target. To better model radar data and to tackle the curse of dimensionality, a three-step classification framework is proposed for UAV detection. First we propose to utilize the greedy subspace clustering to handle potential outliers and the complex sample distribution of radar data. Parameters of the resulting multi-Gaussian model, especially the covariance matrices, could not be reliably estimated due to insufficient training samples and the high dimensionality. Thus, in the second step, a multi-Gaussian subspace reliability analysis is proposed to handle the unreliable feature dimensions of these covariance matrices. To address the challenges of classifying samples using the complex multi-Gaussian model and to fuse the distances of a sample to different clusters at different dimensionalities, a subspace-fusion scheme is proposed in the third step. The proposed approach is validated on a large benchmark dataset, which significantly outperforms the state-of-the-art approaches. |
| first_indexed | 2025-11-14T20:45:41Z |
| format | Article |
| id | nottingham-63878 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:45:41Z |
| publishDate | 2020 |
| publisher | Elsevier Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-638782020-12-03T06:05:32Z https://eprints.nottingham.ac.uk/63878/ A three-step classification framework to handle complex data distribution for radar UAV detection Ren, Jianfeng Jiang, Xudong Unmanned aerial vehicles (UAVs) have been used in a wide range of applications and become an increasingly important radar target. To better model radar data and to tackle the curse of dimensionality, a three-step classification framework is proposed for UAV detection. First we propose to utilize the greedy subspace clustering to handle potential outliers and the complex sample distribution of radar data. Parameters of the resulting multi-Gaussian model, especially the covariance matrices, could not be reliably estimated due to insufficient training samples and the high dimensionality. Thus, in the second step, a multi-Gaussian subspace reliability analysis is proposed to handle the unreliable feature dimensions of these covariance matrices. To address the challenges of classifying samples using the complex multi-Gaussian model and to fuse the distances of a sample to different clusters at different dimensionalities, a subspace-fusion scheme is proposed in the third step. The proposed approach is validated on a large benchmark dataset, which significantly outperforms the state-of-the-art approaches. Elsevier Ltd 2020-10-22 Article PeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/63878/2/A%20three-step%20classification%20framework%20to%20handle%20complex%20data%20distribution%20for%20radar%20UAV%20detection.pdf Ren, Jianfeng and Jiang, Xudong (2020) A three-step classification framework to handle complex data distribution for radar UAV detection. Pattern Recognition, 111 . p. 107709. ISSN 0031-3203 radar UAV detection; micro-Doppler signature; greedysubspace http://dx.doi.org/10.1016/j.patcog.2020.107709 doi:10.1016/j.patcog.2020.107709 doi:10.1016/j.patcog.2020.107709 |
| spellingShingle | radar UAV detection; micro-Doppler signature; greedysubspace Ren, Jianfeng Jiang, Xudong A three-step classification framework to handle complex data distribution for radar UAV detection |
| title | A three-step classification framework to handle complex data distribution for radar UAV detection |
| title_full | A three-step classification framework to handle complex data distribution for radar UAV detection |
| title_fullStr | A three-step classification framework to handle complex data distribution for radar UAV detection |
| title_full_unstemmed | A three-step classification framework to handle complex data distribution for radar UAV detection |
| title_short | A three-step classification framework to handle complex data distribution for radar UAV detection |
| title_sort | three-step classification framework to handle complex data distribution for radar uav detection |
| topic | radar UAV detection; micro-Doppler signature; greedysubspace |
| url | https://eprints.nottingham.ac.uk/63878/ https://eprints.nottingham.ac.uk/63878/ https://eprints.nottingham.ac.uk/63878/ |