A Radar Emitter Recognition Mechanism Based on IFS-Tri-Training Classification Processing
Radar Warning Receiver (RWR) is one of the basic pieces of combat equipment necessary for the electromagnetic situational awareness of aircraft in modern operations and requires good rapid performance and accuracy. This paper proposes a data processing flow for radar warning devices based on a hiera...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/11/7/1078 |
| _version_ | 1797439779270295552 |
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| author | Jundi Wang Xing Wang Yuanrong Tian Zhenkun Chen You Chen |
| author_facet | Jundi Wang Xing Wang Yuanrong Tian Zhenkun Chen You Chen |
| author_sort | Jundi Wang |
| collection | DOAJ |
| description | Radar Warning Receiver (RWR) is one of the basic pieces of combat equipment necessary for the electromagnetic situational awareness of aircraft in modern operations and requires good rapid performance and accuracy. This paper proposes a data processing flow for radar warning devices based on a hierarchical processing mechanism to address the issue of existing algorithms’ inability to balance real-time and accuracy. In the front-level information processing module, multi-attribute decision-making under intuitionistic fuzzy information (IFS) is used to process radar signals with certain prior knowledge to achieve rapid performance. In the post-level information processing module, an improved tri-training method is used to ensure accurate recognition of signals with low pre-level recognition accuracy. To improve the performance of tri-training in identifying radar emitters, the original algorithm is combined with the modified Hyperbolic Tangent Weight (MHTW) to address the problem of data imbalance in the radar identification problem. Simultaneously, cross entropy is employed to enhance the sample selection mechanism, allowing the algorithm to converge rapidly. |
| first_indexed | 2024-03-09T11:57:59Z |
| format | Article |
| id | doaj.art-d4b4cc9364804ddd9598c0ff35f815fe |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T11:57:59Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-d4b4cc9364804ddd9598c0ff35f815fe2023-11-30T23:07:04ZengMDPI AGElectronics2079-92922022-03-01117107810.3390/electronics11071078A Radar Emitter Recognition Mechanism Based on IFS-Tri-Training Classification ProcessingJundi Wang0Xing Wang1Yuanrong Tian2Zhenkun Chen3You Chen4Aviation Engineering School, Air Force Engineering University, 1 Baling Road, Xi’an 710038, ChinaAviation Engineering School, Air Force Engineering University, 1 Baling Road, Xi’an 710038, ChinaSchool of Electronic Countermeasure, National University of Defense Technology, Hefei 230037, ChinaAviation Engineering School, Air Force Engineering University, 1 Baling Road, Xi’an 710038, ChinaAviation Engineering School, Air Force Engineering University, 1 Baling Road, Xi’an 710038, ChinaRadar Warning Receiver (RWR) is one of the basic pieces of combat equipment necessary for the electromagnetic situational awareness of aircraft in modern operations and requires good rapid performance and accuracy. This paper proposes a data processing flow for radar warning devices based on a hierarchical processing mechanism to address the issue of existing algorithms’ inability to balance real-time and accuracy. In the front-level information processing module, multi-attribute decision-making under intuitionistic fuzzy information (IFS) is used to process radar signals with certain prior knowledge to achieve rapid performance. In the post-level information processing module, an improved tri-training method is used to ensure accurate recognition of signals with low pre-level recognition accuracy. To improve the performance of tri-training in identifying radar emitters, the original algorithm is combined with the modified Hyperbolic Tangent Weight (MHTW) to address the problem of data imbalance in the radar identification problem. Simultaneously, cross entropy is employed to enhance the sample selection mechanism, allowing the algorithm to converge rapidly.https://www.mdpi.com/2079-9292/11/7/1078warning deviceemitter identificationhierarchical processingIFS multi-attribute decision-makingtri-trainingcognitive electronic warfare |
| spellingShingle | Jundi Wang Xing Wang Yuanrong Tian Zhenkun Chen You Chen A Radar Emitter Recognition Mechanism Based on IFS-Tri-Training Classification Processing Electronics warning device emitter identification hierarchical processing IFS multi-attribute decision-making tri-training cognitive electronic warfare |
| title | A Radar Emitter Recognition Mechanism Based on IFS-Tri-Training Classification Processing |
| title_full | A Radar Emitter Recognition Mechanism Based on IFS-Tri-Training Classification Processing |
| title_fullStr | A Radar Emitter Recognition Mechanism Based on IFS-Tri-Training Classification Processing |
| title_full_unstemmed | A Radar Emitter Recognition Mechanism Based on IFS-Tri-Training Classification Processing |
| title_short | A Radar Emitter Recognition Mechanism Based on IFS-Tri-Training Classification Processing |
| title_sort | radar emitter recognition mechanism based on ifs tri training classification processing |
| topic | warning device emitter identification hierarchical processing IFS multi-attribute decision-making tri-training cognitive electronic warfare |
| url | https://www.mdpi.com/2079-9292/11/7/1078 |
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