| 摘要: |
| 针对高速飞行器载雷达距离-多普勒（Range-Doppler,RD）图中的干扰分类问题，提出了一种基于多尺度2D熵的干扰分类算法，干扰类型包括梳状谱、噪声卷积、频谱弥散和切片重构。首先，设计了适用于对地雷达RD图的干扰区域提取算法，实现了干扰区域的精准定位。然后，对干扰区域图像进行若干尺度的缩放并使用2D熵算法提取特征。最后，将这些特征拼接后通过支持向量机（Support Vector Machine，SVM）完成分类。算法关键点在于干扰区域的提取和尺度及熵算法的选择。实验结果表明，该方法可有效区分4类干扰，多尺度2D排列熵表现最佳，达到了98.75%的分类准确率。 |
| 关键词: 脉冲多普勒雷达 干扰分类 特征提取 多尺度2D熵 |
| DOI：10.20079/j.issn.1001-893x.240912003 |
|
| 基金项目:国家自然科学基金资助项目(U2230201) |
|
| Application of Multi-scale 2D Entropy in Jamming Classification of Range-Doppler Map |
| WANG Shaoping,ZHANG Zhenwei,LIU Ziyuan,GU Yuantao |
| (1.Tsinghua Shenzhen International Graduate School,Shenzhen 518055,China;2.Department of Electronic Engineering,Tsinghua University,Beijing 100084,China) |
| Abstract: |
| To address the jamming classification problem in range-Doppler(RD) maps of high-speed aircraft-borne radars,a multi-scale 2D entropy-based jamming classification algorithm is proposed for targeting four jamming types,including comb spectrum,noise convolution,spectral dispersion,and slice reconstruction.Firstly,a jamming region extraction algorithm specifically designed for air-to-ground radar RD maps is developed to achieve accurate localization of jamming regions.Subsequently,multi-scale scaling operations are performed on the extracted jamming region images,with feature extraction implemented through a 2D entropy algorithm.Finally,these multi-scale features are then concatenated and classified using a support vector machine(SVM).Key innovations of the algorithm lie on its jamming region extraction methodology and the strategic selection of scale parameters coupled with entropy computation.Experimental results demonstrate the effectiveness of the method in distinguishing the four jamming categories,with the multi-scale 2D permutation entropy approach achieving optimal performance and reaching a classification accuracy of 98.75%. |
| Key words: pulse-Doppler radar jamming classification feature extraction multi-scale 2D entropy |
《电讯技术》编辑部 