模糊函数主脊切面特征提取的局域差分方法  被引量:7

Local Difference Feature Extraction Method for Slice of Ambiguity Function Main Ridge

在线阅读下载全文

作  者:普运伟 马蓝宇 侯文太 张天飞[2] Pu Yunwei;Ma Lanyu;Hou Wentai;Zhang Tianfei(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》2019年第3期386-395,共10页Journal of Data Acquisition and Processing

基  金:国家自然科学基金(61561028)资助项目

摘  要:雷达信号分选是电子对抗的关键技术,提取和补充新的特征参数是解决复杂体制雷达信号分选难题的有效手段。鉴于模糊函数是表征信号内在结构上的有效工具,本文采用改进粒子群算法(Particle swarm optimization,PSO)快速搜索信号的模糊函数主脊切面,并提出一种基于局域差分的模糊函数主脊切面特征提取方法,提取出差值和、差值最大值和差值分布熵3个特征,以表征不同信号波形结构上的局域差异;然后通过模糊C均值算法对提取的特征参数进行聚类性能分析。最后使用LFM,BFSK,CON,QPSK,M SEQ及BPSK共6种典型信号进行实验。实验结果表明,在固定信噪比下,当SNR不低于0dB时,CON,LFM及BFSK信号的平均聚类准确率达到98.7%,6类信号的平均准确率为93.2%。在0~20dB动态信噪比环境下,平均分选准确率仍保持在80.5%以上,且算法具有较好的特征提取时效性,证明了所提方法的可行性和有效性。Radar signal sorting is the key technology of electronic countermeasures.Extracting and supplementing other new feature parameters is the effective measures of solving the sorting problem of complex modulation radar signals.In view of the ambiguity function’s unique effect on characterizing signal inherent structure,this paper adopts the improved particle swarm optimization(PSO)algorithm to search the slice of ambiguity function main ridge of the considered signals,and then proposes a feature extraction method which bases on the local difference to extract three local area characteristics,that is the sum of characteristic value,the maximum characteristic value,and the characteristic value distribution entropy.These features can well reflect the local difference of the signal waveform structure.To verify the feasibility and effectiveness of the proposed method,three simulation experiments are designed and the fuzzy C-means algorithm is used to test the clustering performance of the extracted three feature parameters.The experimental results show that,when SNR is not lower than 0 dB,the average clustering accuracy rate of six kinds of the considered signals,i.e.,LFM,BFSK,CON,QPSK,M-SEQ and BPSK,is 93.2%and the average accuracy of CON,LFM and BFSK signals achieves to 98.7%.When SNR changes from 0 dB to 20 dB,the average clustering accuracy rate keeps above 80.5%.Meanwhile,the time-effectiveness of the proposed model is better than those compared method.These results illustrate the good performance of the extracted local characteristics.

关 键 词:雷达辐射源信号 模糊函数主脊切面 改进粒子群算法 局域差分 

分 类 号:TN974[电子电信—信号与信息处理]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象