Hybrid Model–Data-Driven Radar Jamming Effectiveness Evaluation Method for Accuracy Improvement
Accurate and effective radar jamming effectiveness evaluation is the key to forming the radar jamming OODA (Observe, Orient, Decide, Act) loop. The current model-driven evaluation method has low confidence and the data-driven evaluation method lacks sufficient high-quality data; thus, the evaluation...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/2/258 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832587575609524224 |
|---|---|
| author | Runyang Chen Yi Zhang Xiuhe Li Jinhe Ran Qianqian Shi |
| author_facet | Runyang Chen Yi Zhang Xiuhe Li Jinhe Ran Qianqian Shi |
| author_sort | Runyang Chen |
| collection | DOAJ |
| description | Accurate and effective radar jamming effectiveness evaluation is the key to forming the radar jamming OODA (Observe, Orient, Decide, Act) loop. The current model-driven evaluation method has low confidence and the data-driven evaluation method lacks sufficient high-quality data; thus, the evaluations that are solely model-driven or data-driven are not effective. In order to solve this problem, we propose a radar jamming effectiveness hybrid model–data-driven evaluation method. Firstly, the mechanism of the model is constructed based on the jamming equations. Secondly, the quality of training data is improved by data cleaning and model correction, after which the hybrid model is realized by training with simulated data and fine-tuning it with real-world data. Finally, the validity of the method is proved by simulation experiments, which show that the method is capable of effectively improving the accuracy of prediction and evaluation, and has good practicability. Compared with the model-driven method, the RMSE (Root Mean Square Error) of the prediction results of this method is reduced by 88.26% and the MRE (Mean Relative Error) is reduced by 92.00%. |
| format | Article |
| id | doaj-art-712dd92e50f64ae495bac7f6be6f1984 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-712dd92e50f64ae495bac7f6be6f19842025-01-24T13:47:54ZengMDPI AGRemote Sensing2072-42922025-01-0117225810.3390/rs17020258Hybrid Model–Data-Driven Radar Jamming Effectiveness Evaluation Method for Accuracy ImprovementRunyang Chen0Yi Zhang1Xiuhe Li2Jinhe Ran3Qianqian Shi4College of Electronic Engineering, National University of Defense Technology, Hefei 230037, ChinaCollege of Electronic Engineering, National University of Defense Technology, Hefei 230037, ChinaCollege of Electronic Engineering, National University of Defense Technology, Hefei 230037, ChinaCollege of Electronic Engineering, National University of Defense Technology, Hefei 230037, ChinaCollege of Electronic Engineering, National University of Defense Technology, Hefei 230037, ChinaAccurate and effective radar jamming effectiveness evaluation is the key to forming the radar jamming OODA (Observe, Orient, Decide, Act) loop. The current model-driven evaluation method has low confidence and the data-driven evaluation method lacks sufficient high-quality data; thus, the evaluations that are solely model-driven or data-driven are not effective. In order to solve this problem, we propose a radar jamming effectiveness hybrid model–data-driven evaluation method. Firstly, the mechanism of the model is constructed based on the jamming equations. Secondly, the quality of training data is improved by data cleaning and model correction, after which the hybrid model is realized by training with simulated data and fine-tuning it with real-world data. Finally, the validity of the method is proved by simulation experiments, which show that the method is capable of effectively improving the accuracy of prediction and evaluation, and has good practicability. Compared with the model-driven method, the RMSE (Root Mean Square Error) of the prediction results of this method is reduced by 88.26% and the MRE (Mean Relative Error) is reduced by 92.00%.https://www.mdpi.com/2072-4292/17/2/258jamming effectivenessevaluationhybrid model–data-driven methodradar |
| spellingShingle | Runyang Chen Yi Zhang Xiuhe Li Jinhe Ran Qianqian Shi Hybrid Model–Data-Driven Radar Jamming Effectiveness Evaluation Method for Accuracy Improvement Remote Sensing jamming effectiveness evaluation hybrid model–data-driven method radar |
| title | Hybrid Model–Data-Driven Radar Jamming Effectiveness Evaluation Method for Accuracy Improvement |
| title_full | Hybrid Model–Data-Driven Radar Jamming Effectiveness Evaluation Method for Accuracy Improvement |
| title_fullStr | Hybrid Model–Data-Driven Radar Jamming Effectiveness Evaluation Method for Accuracy Improvement |
| title_full_unstemmed | Hybrid Model–Data-Driven Radar Jamming Effectiveness Evaluation Method for Accuracy Improvement |
| title_short | Hybrid Model–Data-Driven Radar Jamming Effectiveness Evaluation Method for Accuracy Improvement |
| title_sort | hybrid model data driven radar jamming effectiveness evaluation method for accuracy improvement |
| topic | jamming effectiveness evaluation hybrid model–data-driven method radar |
| url | https://www.mdpi.com/2072-4292/17/2/258 |
| work_keys_str_mv | AT runyangchen hybridmodeldatadrivenradarjammingeffectivenessevaluationmethodforaccuracyimprovement AT yizhang hybridmodeldatadrivenradarjammingeffectivenessevaluationmethodforaccuracyimprovement AT xiuheli hybridmodeldatadrivenradarjammingeffectivenessevaluationmethodforaccuracyimprovement AT jinheran hybridmodeldatadrivenradarjammingeffectivenessevaluationmethodforaccuracyimprovement AT qianqianshi hybridmodeldatadrivenradarjammingeffectivenessevaluationmethodforaccuracyimprovement |