Radar Jamming Image Recognition based on Deep Learning and Computer Vision
Radar refers to a new method of detecting the positioning, speed, direction and other characteristics of a target through electromagnetic waves or other wavelengths of radiation within a certain range. In practical applications, interference with electromagnetic signals can cause image distortion. I...
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| Veröffentlicht in: | 2024 International Conference on Integrated Circuits and Communication Systems (ICICACS) S. 1 - 5 |
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23.02.2024
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| Abstract | Radar refers to a new method of detecting the positioning, speed, direction and other characteristics of a target through electromagnetic waves or other wavelengths of radiation within a certain range. In practical applications, interference with electromagnetic signals can cause image distortion. In view of this problem, this paper is based on deep learning and computer vision testing to determine whether the distorted radar image can be correctly recognized under the interference of rain and fog. This article takes aircraft, runways and buildings as examples to identify images under radar interference. The experimental results show that the radar interference image recognition performance based on the convolutional neural network (CNN) algorithm in this paper has the best performance, with a recognition accuracy rate of 98.7%. Our proposed method based on deep learning and computer vision has achieved significant performance improvement in radar interference image recognition tasks. |
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| AbstractList | Radar refers to a new method of detecting the positioning, speed, direction and other characteristics of a target through electromagnetic waves or other wavelengths of radiation within a certain range. In practical applications, interference with electromagnetic signals can cause image distortion. In view of this problem, this paper is based on deep learning and computer vision testing to determine whether the distorted radar image can be correctly recognized under the interference of rain and fog. This article takes aircraft, runways and buildings as examples to identify images under radar interference. The experimental results show that the radar interference image recognition performance based on the convolutional neural network (CNN) algorithm in this paper has the best performance, with a recognition accuracy rate of 98.7%. Our proposed method based on deep learning and computer vision has achieved significant performance improvement in radar interference image recognition tasks. |
| Author | Cheng, Yuxin |
| Author_xml | – sequence: 1 givenname: Yuxin surname: Cheng fullname: Cheng, Yuxin email: 18891566389@163.com organization: China Huayin Ordnance Test Center,Huayin,China |
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| Snippet | Radar refers to a new method of detecting the positioning, speed, direction and other characteristics of a target through electromagnetic waves or other... |
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| SubjectTerms | Computer vision Convolutional Neural Networks Deep learning Image recognition Interference Nonlinear distortion Radar Radar Interference Target recognition |
| Title | Radar Jamming Image Recognition based on Deep Learning and Computer Vision |
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