一种快速搜索模糊函数主脊切面的自适应灰狼算法  

A Self Adaptive GWO for Quickly Searching the Main Ridge Slice of Ambiguity Function

作　　者：郑陵潇 吴海潇 陈磊[2] 普运伟 ZHENG Lingxiao;WU Haixiao;CHEN Lei;PU Yunwei(Computer Center,Kunming University of Science and Technology,Kunming,650500,China;Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming,650500,China)

机构地区：[1]昆明理工大学计算中心,昆明650500 [2]昆明理工大学信息工程与自动化学院,昆明650500

出　　处：《数据采集与处理》2020年第5期892-902,共11页Journal of Data Acquisition and Processing

基　　金：国家自然科学基金(61561028)资助项目。

摘　　要：模糊函数主脊(Ambiguity function main ridge,AFMR)切面特征能较好地反映不同信号结构上的本质差别,是解决当前复杂体制雷达辐射源信号分选难题的可行参数,而快速、智能地搜索模糊函数主脊切面是增加其切面特征实用性的重要问题。为此,本文构建了一种结合均匀初始化策略和改进非线性收敛因子的改进自适应灰狼算法来搜索典型6种雷达辐射源信号的模糊函数主脊切面并提取切面特征,并与穷举法和标准灰狼算法进行对比。实验结果表明,所提方法在搜索AFMR切面并提取特征时,平均耗时仅为1.49 s,相较于穷举法和标准灰狼优化算法,效率分别提高了75.7%和19.0%,具有较优的时效性。在固定信噪比(Signal⁃to⁃noise ratio,SNR)环境下,当SNR不低于0 dB时,提取到特征值的平均聚类准确率为96.4%,在0~20 dB动态信噪比环境下,平均聚类准确率可达95.2%,具有较好的准确性、抗噪性能及较强的类内聚集性和类间分离能力,证实了所提方法的可行性与有效性。The feature of the slice of ambiguity function main ridge can better reflect the structural essential differences between signals,and it is a feasible parameter to solve the current complex system radar emitter signal sorting problem.The fast and intelligent search for the slice of ambiguity function main ridge is an important issue to increase the practicability of its feature of the slice.In this paper,an improved selfadaptive grey wolf optimization(GWO)combining uniform initialization strategy and improved nonlinear convergence factor was proposed to search the main ridge slice of ambiguity function of six typical radar emitter signals and extract the feature of slices,which were compared with the exhaustive method and standard GWO.The experimental results showed that the average time consumption of the proposed method was only 1.49 s when searching AFMR slice and extracting feature.Compared with the exhaustive method and the standard GWO,the efficiency was improved by 75.7%and 19.0%,respectively,with better timeliness.In a fixed SNR environment,when the SNR was not less than 0 dB,the average clustering accuracy of the extracted feature values was 96.4%;and in a dynamic SNR environment of 0—20 dB,the average clustering accuracy can reach 95.2%,with good accuracy,anti-noise performance and strong intra-class aggregation and inter-class separation ability,which proves the feasibility and effectiveness of the proposed method.

关 键 词：雷达辐射源信号 模糊函数 自适应灰狼算法 搜索主脊切面 提取特征 

分 类 号：TN974[电子电信—信号与信息处理]