模糊函数主脊切面特征提取的局域差分方法
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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0 dB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0～20 dB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 to20 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.
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 to20 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.
引文
[1]李伟,刘海起,魏方圆,等.雷达信号分选关键技术探究[J].通讯世界, 2017(10):243-244.Li Wei, Liu Haiqi, Wei Fangyuan. et al. Research on key technologies of radar signal sorting[J]. Communication World, 2017(10):243-244.
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[4]王世强,张登福,毕笃彦,等.一种低复杂度的雷达信号分选方法[J].西安电子科技大学学报,2011,38(4):148-153.Wang Shiqiang, Zhang Dengfu, Bi Duyan, et al. Novel radar signal sorting method with low complexity[J]. Journal of Xidian University,2011,38(4):148-153.
[5]王磊.雷达辐射源个体识别的方法研究[D].西安:西安电子科技大学,2011.Wang Lei. On methods for Specific Radar Emitter Identification[D]. Xi’an:Xidian University, 2011.
[6]普运伟,金炜东,朱明,等.雷达辐射源信号模糊函数主脊切面特征提取方法[J].红外与毫米报, 2008, 27(2):133-137.Pu Yunwei, Jin Weidong, Zhu Ming, et al. Extracting the main ridge slice characteristics of ambiguity function for radar emitter signals[J]. Journal of Infrared and Millimeter Waves, 2008, 27(2):133-137.
[7]李林,姬红兵.基于模糊函数的雷达辐射源个体识别[J].电子与信息学报,2009,31(11):2546-2551.Li Lin, Ji Hongbing. Specific emitter identification based on ambiguity function[J]. Journal of Electronics and Information Technology,2009,31(11):2546-2551.
[8]时羽.雷达辐射源信号AFMR切面智能搜索与分选特征提取[D].昆明:昆明理工大学, 2013.Shi Yu. Intelligent search method and selection feature extraction of AFMR of radar emitter signal[D]. Kunming:Kunming University of Science and Technology,2013.
[9]谢晓峰,张文俊,杨之廉.微粒群算法综述[J].控制与决策, 2003, 18(2):129-134.Xie Xiaofeng, Zhang Wenjun, Yang Zhilian. Overview of particle swarm optimization[J]. Control and Decision, 2003, 18(2):129-134.
[10] Shi Y H, Eberhart R C. Parameter selection in particle swarm optimization[C]//Proceedings of the 7th Annual Conference on Evolutionary Programming. Washington DC, USA:[s.n.], 1998:591-560.
[11] Babaee H, Khosravi A. An improve PSO based hybrid algorithms[C]//Proceedings of IEEE International Conference on Management and Service Science. Wuhan, China:IEEE, 2011:1-5.
[12] Pu Yunwei, Zhang Tianfei, Shi Yu. An improved PSO algorithm and its application in fast feature extraction of radar emitter signals[C]//Proceedings of the IEEE International Conference on Natural Computation. Chongqing, China:IEEE, 2012:1127-1130.
[13]张天飞,普运伟,时羽.模糊函数主脊切面特征提取的粒子群优化方法[J].计算机工程与应用,2012,48(34):158-161,165.Zhang Tianfei, Pu Yunwei, Shi Yu. Extracting slice of ambiguity function main ridge using improved particle swarm optimization[J]. Computer Engineering and Applications,2012,48(34):158-161,165.
[14]普运伟.复杂体制雷达辐射源信号分选模型与算法研究[D].成都:西南交通大学,2007.Pu Yunwei. Deinterleaving models and algorithms for advanced radar emitter signals[D].Chengdu:Southwest Jiaotong University,2007.
[15]徐璟,何明浩,陈昌孝,等.雷达辐射源特征参数提取算法复杂度分析[J].中国电子科学研究院学报, 2013,8(1):43-47.Xu Jing, He Minghao, Chen Changxiao, et al. Algorithm complexity analysis of radar emitter characteristic parameter extraction[J]. Journal of CAEIT, 2013,8(1):43-47.
[16]卢一相,高清维.基于WTMM与多重分形的信号毛刺去除方法[J].中国科学技术大学学报,2010,40(11):1142-1147.Lu Yixiang, Gao Qingwei. An outlier removal method based on WTMM and multifractal[J]. Journal of University of Science and Technology of China, 2010, 40(11):1142-1147.
[17]田慧.基于分数阶Fourier变换的雷达目标检测算法的研究[D].大连:大连海事大学,2017.Tian Hui. Research of radar target detection algorithm based on FRFT[D]. Dalian:Dalian Maritime University, 2017.
[2]柳征,姜文利,周一宇.基于小波包变换的辐射源信号识别[J].信号处理,2005,21(5):460-464.Liu Zheng, Jiang Wenli, Zhou Yiyu. Emitter signals recognition based on wavelet packet transform[J]. Signal Processing,2005, 21(5):460-464.
[3]朱明,普运伟,金炜东,等.基于时频原子方法的雷达辐射源信号特征提取[J].电波科学学报, 2007, 22(3):458-462.Zhu Ming, Pu Yunwei, Jin Weidong, et al. Feature extraction of radar emitter signals based on time-frequency atoms[J]. Chinese Journal of Radio Science, 2007, 22(3):458-462.
[4]王世强,张登福,毕笃彦,等.一种低复杂度的雷达信号分选方法[J].西安电子科技大学学报,2011,38(4):148-153.Wang Shiqiang, Zhang Dengfu, Bi Duyan, et al. Novel radar signal sorting method with low complexity[J]. Journal of Xidian University,2011,38(4):148-153.
[5]王磊.雷达辐射源个体识别的方法研究[D].西安:西安电子科技大学,2011.Wang Lei. On methods for Specific Radar Emitter Identification[D]. Xi’an:Xidian University, 2011.
[6]普运伟,金炜东,朱明,等.雷达辐射源信号模糊函数主脊切面特征提取方法[J].红外与毫米报, 2008, 27(2):133-137.Pu Yunwei, Jin Weidong, Zhu Ming, et al. Extracting the main ridge slice characteristics of ambiguity function for radar emitter signals[J]. Journal of Infrared and Millimeter Waves, 2008, 27(2):133-137.
[7]李林,姬红兵.基于模糊函数的雷达辐射源个体识别[J].电子与信息学报,2009,31(11):2546-2551.Li Lin, Ji Hongbing. Specific emitter identification based on ambiguity function[J]. Journal of Electronics and Information Technology,2009,31(11):2546-2551.
[8]时羽.雷达辐射源信号AFMR切面智能搜索与分选特征提取[D].昆明:昆明理工大学, 2013.Shi Yu. Intelligent search method and selection feature extraction of AFMR of radar emitter signal[D]. Kunming:Kunming University of Science and Technology,2013.
[9]谢晓峰,张文俊,杨之廉.微粒群算法综述[J].控制与决策, 2003, 18(2):129-134.Xie Xiaofeng, Zhang Wenjun, Yang Zhilian. Overview of particle swarm optimization[J]. Control and Decision, 2003, 18(2):129-134.
[10] Shi Y H, Eberhart R C. Parameter selection in particle swarm optimization[C]//Proceedings of the 7th Annual Conference on Evolutionary Programming. Washington DC, USA:[s.n.], 1998:591-560.
[11] Babaee H, Khosravi A. An improve PSO based hybrid algorithms[C]//Proceedings of IEEE International Conference on Management and Service Science. Wuhan, China:IEEE, 2011:1-5.
[12] Pu Yunwei, Zhang Tianfei, Shi Yu. An improved PSO algorithm and its application in fast feature extraction of radar emitter signals[C]//Proceedings of the IEEE International Conference on Natural Computation. Chongqing, China:IEEE, 2012:1127-1130.
[13]张天飞,普运伟,时羽.模糊函数主脊切面特征提取的粒子群优化方法[J].计算机工程与应用,2012,48(34):158-161,165.Zhang Tianfei, Pu Yunwei, Shi Yu. Extracting slice of ambiguity function main ridge using improved particle swarm optimization[J]. Computer Engineering and Applications,2012,48(34):158-161,165.
[14]普运伟.复杂体制雷达辐射源信号分选模型与算法研究[D].成都:西南交通大学,2007.Pu Yunwei. Deinterleaving models and algorithms for advanced radar emitter signals[D].Chengdu:Southwest Jiaotong University,2007.
[15]徐璟,何明浩,陈昌孝,等.雷达辐射源特征参数提取算法复杂度分析[J].中国电子科学研究院学报, 2013,8(1):43-47.Xu Jing, He Minghao, Chen Changxiao, et al. Algorithm complexity analysis of radar emitter characteristic parameter extraction[J]. Journal of CAEIT, 2013,8(1):43-47.
[16]卢一相,高清维.基于WTMM与多重分形的信号毛刺去除方法[J].中国科学技术大学学报,2010,40(11):1142-1147.Lu Yixiang, Gao Qingwei. An outlier removal method based on WTMM and multifractal[J]. Journal of University of Science and Technology of China, 2010, 40(11):1142-1147.
[17]田慧.基于分数阶Fourier变换的雷达目标检测算法的研究[D].大连:大连海事大学,2017.Tian Hui. Research of radar target detection algorithm based on FRFT[D]. Dalian:Dalian Maritime University, 2017.
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