Radar Active Electromagnetic Interference Visual Detection and Parameter Estimation Method

ZHU Xuan; RUAN Jiang-jun; WU Hao; SHI Zhao-peng; HE Fang-min; MENG Jin

doi:10.12263/DZXB.20240875

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Radar Active Electromagnetic Interference Visual Detection and Parameter Estimation Method

PAPERS | 更新时间：2025-07-24

- Radar Active Electromagnetic Interference Visual Detection and Parameter Estimation Method
- ACTA ELECTRONICA SINICA Vol. 53, Issue 4, Pages: 1251-1263(2025)
- 作者机构：
  
  1.武汉大学电气与自动化学院，湖北武汉 430073
  2.海军工程大学电磁能技术全国重点实验室，湖北武汉 430033
  3.华中科技大学电子信息与通信学院，湖北武汉 430074
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62271497;61801502;52025072)
- DOI：10.12263/DZXB.20240875
  CLC： TN974;
- Received：28 September 2024，
  
  Revised：2025-01-23，
  
  Published：25 April 2025
- 稿件说明：
移动端阅览
朱轩, 阮江军, 吴灏, 等. 雷达有源电磁干扰视觉检测与参数估计方法[J]. 电子学报, 2025, 53(04): 1251-1263.

ZHU Xuan, RUAN Jiang-jun, WU Hao, et al. Radar Active Electromagnetic Interference Visual Detection and Parameter Estimation Method[J]. Acta Electronica Sinica, 2025, 53(04): 1251-1263.
朱轩, 阮江军, 吴灏, 等. 雷达有源电磁干扰视觉检测与参数估计方法[J]. 电子学报, 2025, 53(04): 1251-1263. DOI：10.12263/DZXB.20240875

ZHU Xuan, RUAN Jiang-jun, WU Hao, et al. Radar Active Electromagnetic Interference Visual Detection and Parameter Estimation Method[J]. Acta Electronica Sinica, 2025, 53(04): 1251-1263. DOI：10.12263/DZXB.20240875

摘要

面向高动态变化、时频混叠、未知干扰等因素，本文提出一种雷达有源电磁干扰视觉检测与参数估计方法，旨在提升雷达系统电磁兼容性与抗干扰能力.首先，基于电磁干扰信号建模仿真构建时频图像数据集，并利用自适应对比度与边缘增强网络（Adaptive Contrast and Edge Enhancement Network，ACEENet）进行预处理，强化边缘细节并抑制噪声；然后，利用所提降参增强网络（Parameter Reduction Enhancement Network，PRENet）、嵌入三重注意力机制的Slim-Neck（Slim-Neck with Triplet Attention Mechanism，Slim-Neck-TAM）与组合损失函数改进YOLOv8n目标检测算法，构建高精度电磁干扰视觉检测网络（Electromagnetic Interference Visual Detection Network，EIVDNet），实现干扰信号的模式识别与定位；最终，基于位置信息与参数估计原理实现干扰信号关键参数粗估计，并通过XGBoost回归算法修正后获得精确估计值.实验结果表明，所提方法电磁干扰信号检测精度与速度能够达到99.30%和82.75帧/秒，且参数估计总误差为1.01%，在低信噪比/干噪比与未知干扰情况下依然具有良好的感知性能，有助于提高雷达认知智能水平.

Abstract

Towards the factors such as high dynamic variation

time-frequency aliasing and unknown interference

this paper proposes a radar active electromagnetic interference visual detection and parameter estimation method

aiming to improve the electromagnetic compatibility and anti-jamming ability of the radar system. Firstly

the time-frequency image dataset is constructed based on the modelling and simulation of electromagnetic interference signals

and the adaptive contrast and edge enhancement network (ACEENet) is used for preprocessing to strengthen the edge details and suppress noise. Then

the proposed parameter reduction enhancement network (PRENet)

slim-neck with triplet attention mechanism (Slim-Neck-TAM) and combined loss function are used to improve the YOLOv8n object detection algorithm

and a high-precision electromagnetic interference visual detection network (EIVDNet) is constructed to obtain the pattern and location of interference signals. Finally

based on the location information and parameter estimation principle

the rough estimation of the key parameters of the interference signal is realized

and the accurate estimation value is obtained after correction by the XGBoost regression algorithm. The results show that the detection precision and speed of the electromagnetic interference signal can reach 99.30% and 82.75 frames/s

and the overall error rate of parameter estimation is 1.01%

which has favourable perception performance under low signal-to-noise ratio / jamming-to-noise ratio and unknown interference and can be conducive to improve the level of radar cognitive intelligence.

关键词

Keywords

references

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