摘要
为提高瞬态干扰处理的稳健性,超视距雷达可采用先识别后抑制的思路.本文研究基于距离-多普勒(Range⁃Doppler,RD)图的瞬态干扰自动识别方法,将RD图转化为灰度图,提取其纹理特征,再基于机器学习设计分类算法.首先,提出新的瞬态干扰模型,仿真产生干扰数据,避免训练依赖实测数据;其次,建立RD灰度图图库,分强干扰、弱干扰和无干扰三类情况;然后,提取局部二值模式(Local Binary Pattern,LBP)纹理特征,基于支持向量机设计二分类器,结合纠错输出编码设计三分类器.最后,通过实测数据和文献RD图,验证本文所提识别方法的准确性,并比较分析不同图像特征及参数的影响.
To improve the robustness of transient interference processing,over⁃the⁃horizon radar(OTHR)can take the way of suppression after assured detection.This paper analyzes automatic recognition of transient interference in the range⁃Doppler(RD)map,by transforming the RD map into gray image,extracting the texture features,and designing the classification algorithm based on machine learning.Firstly,a model of transient interference is developed to simulate the re⁃ceived data,so that the training does not rely on real data.Secondly,the image datasets are produced and classified into three categories,i.e.strong interference,weak interference,and non⁃interference.Then,the local binary pattern(LBP)tex⁃ture features are extracted to design the binary classifier based on support vector machine(SVM)and then design the terna⁃ry classifier by error⁃correcting output codes(ECOC).Finally,simulations based on real data from OTHR and literatures demonstrate the effectiveness of our method and the effects of various parameters and image features.
作者
罗忠涛
夏杭
卢琨
何子述
LUO Zhong-tao;XIA Hang;LU Kun;HE Zi-shu(School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;Nanjing Research Institute of Electronics Technology,Nanjing,Jiangsu 210013,China;School of Information and Communication Engineering,University of Electronic Science and Technology of China,Chengdu,Sichuan 611731,China)
出处
《电子学报》
EI
CAS
CSCD
北大核心
2021年第7期1279-1285,共7页
Acta Electronica Sinica
基金
国家自然科学基金(No.61701067,No.61702065)。
