TR-RAGCN-AFF-RESS: A Method for Radar Emitter Signal Sorting
Radar emitter signal sorting (RESS) is a crucial process in contemporary electronic battlefield situation awareness. Separating pulses belonging to the same radar emitter from interleaved radar pulse sequences with a lack of prior information, high density, strong overlap, and wide parameter distrib...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/7/1121 |
| _version_ | 1827286668582846464 |
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| author | Zhizhong Zhang Xiaoran Shi Xinyi Guo Feng Zhou |
| author_facet | Zhizhong Zhang Xiaoran Shi Xinyi Guo Feng Zhou |
| author_sort | Zhizhong Zhang |
| collection | DOAJ |
| description | Radar emitter signal sorting (RESS) is a crucial process in contemporary electronic battlefield situation awareness. Separating pulses belonging to the same radar emitter from interleaved radar pulse sequences with a lack of prior information, high density, strong overlap, and wide parameter distribution has attracted increasing attention. In order to improve the accuracy of RESS under scenarios with limited labeled samples, this paper proposes an RESS model called TR-RAGCN-AFF-RESS. This model transforms the RESS problem into a pulse-by-pulse classification task. Firstly, a novel weighted adjacency matrix construction method was proposed to characterize the structural relationships between pulse attribute parameters more accurately. Building upon this foundation, two networks were developed: a Transformer(TR)-based interleaved pulse sequence temporal feature extraction network and a residual attention graph convolutional network (RAGCN) for extracting the structural relationship features of attribute parameters. Finally, the attention feature fusion (AFF) algorithm was introduced to fully integrate the temporal features and attribute parameter structure relationship features, enhancing the richness of feature representation for the original pulses and achieving more accurate sorting results. Compared to existing deep learning-based RESS algorithms, this method does not require many labeled samples for training, making it better suited for scenarios with limited labeled sample availability. Experimental results and analysis confirm that even with only 10% of the training samples, this method achieves a sorting accuracy exceeding 93.91%, demonstrating high robustness against measurement errors, lost pulses, and spurious pulses in non-ideal conditions. |
| first_indexed | 2024-04-24T10:36:13Z |
| format | Article |
| id | doaj.art-e401d4420f174691a4d3d725979b6451 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-24T10:36:13Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-e401d4420f174691a4d3d725979b64512024-04-12T13:25:23ZengMDPI AGRemote Sensing2072-42922024-03-01167112110.3390/rs16071121TR-RAGCN-AFF-RESS: A Method for Radar Emitter Signal SortingZhizhong Zhang0Xiaoran Shi1Xinyi Guo2Feng Zhou3Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education, Xidian University, Xi’an 710071, ChinaKey Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education, Xidian University, Xi’an 710071, ChinaKey Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education, Xidian University, Xi’an 710071, ChinaKey Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education, Xidian University, Xi’an 710071, ChinaRadar emitter signal sorting (RESS) is a crucial process in contemporary electronic battlefield situation awareness. Separating pulses belonging to the same radar emitter from interleaved radar pulse sequences with a lack of prior information, high density, strong overlap, and wide parameter distribution has attracted increasing attention. In order to improve the accuracy of RESS under scenarios with limited labeled samples, this paper proposes an RESS model called TR-RAGCN-AFF-RESS. This model transforms the RESS problem into a pulse-by-pulse classification task. Firstly, a novel weighted adjacency matrix construction method was proposed to characterize the structural relationships between pulse attribute parameters more accurately. Building upon this foundation, two networks were developed: a Transformer(TR)-based interleaved pulse sequence temporal feature extraction network and a residual attention graph convolutional network (RAGCN) for extracting the structural relationship features of attribute parameters. Finally, the attention feature fusion (AFF) algorithm was introduced to fully integrate the temporal features and attribute parameter structure relationship features, enhancing the richness of feature representation for the original pulses and achieving more accurate sorting results. Compared to existing deep learning-based RESS algorithms, this method does not require many labeled samples for training, making it better suited for scenarios with limited labeled sample availability. Experimental results and analysis confirm that even with only 10% of the training samples, this method achieves a sorting accuracy exceeding 93.91%, demonstrating high robustness against measurement errors, lost pulses, and spurious pulses in non-ideal conditions.https://www.mdpi.com/2072-4292/16/7/1121radar emitter signal sorting (RESS)temporal featuresattribute parameter structure relationshipattention feature fusion (AFF) |
| spellingShingle | Zhizhong Zhang Xiaoran Shi Xinyi Guo Feng Zhou TR-RAGCN-AFF-RESS: A Method for Radar Emitter Signal Sorting Remote Sensing radar emitter signal sorting (RESS) temporal features attribute parameter structure relationship attention feature fusion (AFF) |
| title | TR-RAGCN-AFF-RESS: A Method for Radar Emitter Signal Sorting |
| title_full | TR-RAGCN-AFF-RESS: A Method for Radar Emitter Signal Sorting |
| title_fullStr | TR-RAGCN-AFF-RESS: A Method for Radar Emitter Signal Sorting |
| title_full_unstemmed | TR-RAGCN-AFF-RESS: A Method for Radar Emitter Signal Sorting |
| title_short | TR-RAGCN-AFF-RESS: A Method for Radar Emitter Signal Sorting |
| title_sort | tr ragcn aff ress a method for radar emitter signal sorting |
| topic | radar emitter signal sorting (RESS) temporal features attribute parameter structure relationship attention feature fusion (AFF) |
| url | https://www.mdpi.com/2072-4292/16/7/1121 |
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