Semhybridnet: a semantically enhanced hybrid CNN-transformer network for radar pulse image segmentation
Abstract Radar signal sorting is a vital component of electronic warfare reconnaissance, serving as the basis for identifying the source of radar signals. However, traditional radar signal sorting methods are increasingly inadequate and computationally complex in modern electromagnetic environments....
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2023-12-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-023-01294-y |
| _version_ | 1797233160327528448 |
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| author | Hongjia Liu Yubin Xiao Xuan Wu Yuanshu Li Peng Zhao Yanchun Liang Liupu Wang You Zhou |
| author_facet | Hongjia Liu Yubin Xiao Xuan Wu Yuanshu Li Peng Zhao Yanchun Liang Liupu Wang You Zhou |
| author_sort | Hongjia Liu |
| collection | DOAJ |
| description | Abstract Radar signal sorting is a vital component of electronic warfare reconnaissance, serving as the basis for identifying the source of radar signals. However, traditional radar signal sorting methods are increasingly inadequate and computationally complex in modern electromagnetic environments. To address this issue, this paper presents a novel machine-learning-based approach for radar signal sorting. Our method utilizes SemHybridNet, a Semantically Enhanced Hybrid CNN-Transformer Network, for the classification of semantic information in two-dimensional radar pulse images obtained by converting the original radar data. SemHybridNet incorporates two innovative modules: one for extracting period structure features, and the other for ensuring effective integration of local and global features. Notably, SemHybridNet adopts an end-to-end structure, eliminating the need for repetitive looping over the original sequence and reducing computational complexity. We evaluate the performance of our method through conducting comprehensive comparative experiments. The results demonstrate our method significantly outperforms the traditional methods, particularly in environments with high missing and noise pulse rates. Moreover, the ablation studies confirm the effectiveness of these two proposed modules in enhancing the performance of SemHybridNet. In conclusion, our method holds promise for enhancing electronic warfare reconnaissance capabilities and opens new avenues for future research in this field. |
| first_indexed | 2024-04-24T16:11:45Z |
| format | Article |
| id | doaj.art-148821b1834a47f9a9c079defcf4bb4f |
| institution | Directory Open Access Journal |
| issn | 2199-4536 2198-6053 |
| language | English |
| last_indexed | 2024-04-24T16:11:45Z |
| publishDate | 2023-12-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj.art-148821b1834a47f9a9c079defcf4bb4f2024-03-31T11:39:35ZengSpringerComplex & Intelligent Systems2199-45362198-60532023-12-011022851286810.1007/s40747-023-01294-ySemhybridnet: a semantically enhanced hybrid CNN-transformer network for radar pulse image segmentationHongjia Liu0Yubin Xiao1Xuan Wu2Yuanshu Li3Peng Zhao4Yanchun Liang5Liupu Wang6You Zhou7Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin UniversityKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin UniversityKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin UniversityKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin UniversityKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin UniversitySchool of Computer Science, Zhuhai College of Science and TechnologyKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin UniversityKey Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin UniversityAbstract Radar signal sorting is a vital component of electronic warfare reconnaissance, serving as the basis for identifying the source of radar signals. However, traditional radar signal sorting methods are increasingly inadequate and computationally complex in modern electromagnetic environments. To address this issue, this paper presents a novel machine-learning-based approach for radar signal sorting. Our method utilizes SemHybridNet, a Semantically Enhanced Hybrid CNN-Transformer Network, for the classification of semantic information in two-dimensional radar pulse images obtained by converting the original radar data. SemHybridNet incorporates two innovative modules: one for extracting period structure features, and the other for ensuring effective integration of local and global features. Notably, SemHybridNet adopts an end-to-end structure, eliminating the need for repetitive looping over the original sequence and reducing computational complexity. We evaluate the performance of our method through conducting comprehensive comparative experiments. The results demonstrate our method significantly outperforms the traditional methods, particularly in environments with high missing and noise pulse rates. Moreover, the ablation studies confirm the effectiveness of these two proposed modules in enhancing the performance of SemHybridNet. In conclusion, our method holds promise for enhancing electronic warfare reconnaissance capabilities and opens new avenues for future research in this field.https://doi.org/10.1007/s40747-023-01294-ySemantic segmentationConvolutional neural networkTransformerRadar pulse image segmentation |
| spellingShingle | Hongjia Liu Yubin Xiao Xuan Wu Yuanshu Li Peng Zhao Yanchun Liang Liupu Wang You Zhou Semhybridnet: a semantically enhanced hybrid CNN-transformer network for radar pulse image segmentation Complex & Intelligent Systems Semantic segmentation Convolutional neural network Transformer Radar pulse image segmentation |
| title | Semhybridnet: a semantically enhanced hybrid CNN-transformer network for radar pulse image segmentation |
| title_full | Semhybridnet: a semantically enhanced hybrid CNN-transformer network for radar pulse image segmentation |
| title_fullStr | Semhybridnet: a semantically enhanced hybrid CNN-transformer network for radar pulse image segmentation |
| title_full_unstemmed | Semhybridnet: a semantically enhanced hybrid CNN-transformer network for radar pulse image segmentation |
| title_short | Semhybridnet: a semantically enhanced hybrid CNN-transformer network for radar pulse image segmentation |
| title_sort | semhybridnet a semantically enhanced hybrid cnn transformer network for radar pulse image segmentation |
| topic | Semantic segmentation Convolutional neural network Transformer Radar pulse image segmentation |
| url | https://doi.org/10.1007/s40747-023-01294-y |
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