Recognition of Ballistic Targets by Fusing Micro-Motion Features with Networks
Ballistic target recognition is of great significance for space attack and defense. The micro-motion features, which contain spatial and motion information, can be regarded as the foundation of the recognition of ballistic targets. To take full advantage of the micro-motion information of ballistic...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/22/5678 |
| _version_ | 1797464120239325184 |
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| author | Lei Yang Wenpeng Zhang Weidong Jiang |
| author_facet | Lei Yang Wenpeng Zhang Weidong Jiang |
| author_sort | Lei Yang |
| collection | DOAJ |
| description | Ballistic target recognition is of great significance for space attack and defense. The micro-motion features, which contain spatial and motion information, can be regarded as the foundation of the recognition of ballistic targets. To take full advantage of the micro-motion information of ballistic targets, this paper proposes a method based on feature fusion to recognize ballistic targets. The proposed method takes two types of data as input: the time–range (TR) map and the time–frequency (TF) spectrum. An improved feature extraction module based on 1D convolution and time self-attention is applied first to extract the multi-level features at each time instant and the global temporal information. Then, to efficiently fuse the features extracted from the TR map and TF spectrum, deep generalized canonical correlation analysis with center loss (DGCCA-CL) is proposed to transform the extracted features into a hidden space. The proposed DGCCA-CL possesses better performance in two aspects: small intra-class distance and compact representation, which is crucial to the fusion of multi-modality data. At last, the attention mechanism-based classifier which can adaptively focus on the important features is employed to give the target types. Experiment results show that the proposed method outperforms other network-based recognition methods. |
| first_indexed | 2024-03-09T18:02:25Z |
| format | Article |
| id | doaj.art-d9d2ee10216949b1a5b956cfd865798d |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T18:02:25Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d9d2ee10216949b1a5b956cfd865798d2023-11-24T09:48:41ZengMDPI AGRemote Sensing2072-42922022-11-011422567810.3390/rs14225678Recognition of Ballistic Targets by Fusing Micro-Motion Features with NetworksLei Yang0Wenpeng Zhang1Weidong Jiang2College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaBallistic target recognition is of great significance for space attack and defense. The micro-motion features, which contain spatial and motion information, can be regarded as the foundation of the recognition of ballistic targets. To take full advantage of the micro-motion information of ballistic targets, this paper proposes a method based on feature fusion to recognize ballistic targets. The proposed method takes two types of data as input: the time–range (TR) map and the time–frequency (TF) spectrum. An improved feature extraction module based on 1D convolution and time self-attention is applied first to extract the multi-level features at each time instant and the global temporal information. Then, to efficiently fuse the features extracted from the TR map and TF spectrum, deep generalized canonical correlation analysis with center loss (DGCCA-CL) is proposed to transform the extracted features into a hidden space. The proposed DGCCA-CL possesses better performance in two aspects: small intra-class distance and compact representation, which is crucial to the fusion of multi-modality data. At last, the attention mechanism-based classifier which can adaptively focus on the important features is employed to give the target types. Experiment results show that the proposed method outperforms other network-based recognition methods.https://www.mdpi.com/2072-4292/14/22/5678ballistic target recognitionmicro-Dopplerfeature fusiondeep generalized canonical correlation analysiscenter loss |
| spellingShingle | Lei Yang Wenpeng Zhang Weidong Jiang Recognition of Ballistic Targets by Fusing Micro-Motion Features with Networks Remote Sensing ballistic target recognition micro-Doppler feature fusion deep generalized canonical correlation analysis center loss |
| title | Recognition of Ballistic Targets by Fusing Micro-Motion Features with Networks |
| title_full | Recognition of Ballistic Targets by Fusing Micro-Motion Features with Networks |
| title_fullStr | Recognition of Ballistic Targets by Fusing Micro-Motion Features with Networks |
| title_full_unstemmed | Recognition of Ballistic Targets by Fusing Micro-Motion Features with Networks |
| title_short | Recognition of Ballistic Targets by Fusing Micro-Motion Features with Networks |
| title_sort | recognition of ballistic targets by fusing micro motion features with networks |
| topic | ballistic target recognition micro-Doppler feature fusion deep generalized canonical correlation analysis center loss |
| url | https://www.mdpi.com/2072-4292/14/22/5678 |
| work_keys_str_mv | AT leiyang recognitionofballistictargetsbyfusingmicromotionfeatureswithnetworks AT wenpengzhang recognitionofballistictargetsbyfusingmicromotionfeatureswithnetworks AT weidongjiang recognitionofballistictargetsbyfusingmicromotionfeatureswithnetworks |