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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Bibliographic Details
Published in:2024 International Conference on Integrated Circuits and Communication Systems (ICICACS) pp. 1 - 5
Main Author: Cheng, Yuxin
Format: Conference Proceeding
Language:English
Published: IEEE 23.02.2024
Subjects:
Computer vision
Convolutional Neural Networks
Deep learning
Image recognition
Interference
Nonlinear distortion
Radar
Radar Interference
Target recognition
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Summary: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.
DOI:10.1109/ICICACS60521.2024.10498348