上海交通大学学报

上海交通大学学报 >

2017 , Vol. 51 >Issue 9: 1124 - 1130

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2017.09.016

兵器工业

 基于改进遗传算法和SinChirplet原子的调频
雷达信号稀疏分解

展开
  •  空军工程大学  航空航天工程学院

网络出版日期: 2017-09-20

基金资助

 国家自然科学基金(61372167) ，航空科学基金(20152096019)资助

收起

 Sparse Decomposition for Frequency Modulation Radar Signal Based on
 Advanced Genetic Algorithm and SinChirplet Atom

Expand
  •  Aeronautics and Astronautics Engineering College, Air Force Engineering University

Online published: 2017-09-20

Supported by

 

Fold

摘要

 为构建合适的时频原子库和信号分解算法，提出一种基于改进遗传算法和SinChirplet原子的调频雷达信号稀疏分解算法.这种新的SinChirplet原子在Chirplet原子的基础上增加正弦调频因子，改善原子时频曲线的弯曲性能，使原子对非线性调频信号具有较强的匹配性能.然后基于原子的匹配特性，改进遗传算法中初始原子种群产生机制，提高了最佳原子搜索速度.理论分析和仿真结果表明，基于改进遗传算法的信号稀疏分解效率高于传统遗传算法和匹配追踪算法.相比现有的3种典型时频原子，SinChirplet原子的匹配性能良好，可以更有效地分解调频雷达信号及其混合信号.

关键词：  ; 时频原子; 信号分解; 改进遗传算法; 调频信号; 稀疏表示; 电子干扰

本文引用格式

王星，周一鹏，田元荣，陈游，周东青，贺继渊 .  基于改进遗传算法和SinChirplet原子的调频
雷达信号稀疏分解[J]. 上海交通大学学报, 2017 , 51(9) : 1124 -1130 . DOI: 10.16183/j.cnki.jsjtu.2017.09.016

Abstract

  In order to improve the performance of sparse decomposition for radar signals, a signal sparse decomposition algorithm(SDA) based on a novel SinChirplet atomic dictionary and an advanced genetic algorithm(GA) is proposed. Firstly, a sinusoidal frequency modulating factor is added into Chirplet atom so that the curvature performance of the atom timefrequency curve can be improved. Therefore SinChirplet atom has good matching performance for nonlinear frequency modulation signals. Secondly, based on the matching characteristic of SinChirplet atom, the generation of atom initialization population in GA is improved so that the bestatom searching speed can be raised. The theoretical analysis and the simulation results show that the decomposition efficiency of advanced GA is higher than that of GA and matching pursuit(MP) algorithm. Meanwhile, the matching performance and the decomposition efficiency of SinChirplet atom are better than those of other three traditional timefrequency atoms.

Key words：  timefrequency atom; signal decomposition; advanced genetic algorithm; frequency modulation signal; sparse representation; electronic jamming

参考文献

 ［1］张淑宁, 朱航, 赵惠昌, 等. 基于周期模糊函数的伪码调相与正弦调频复合引信信号参数提取技术［J］. 兵工学报, 2014, 35(5): 627633.
ZHANG Shuning, ZHU Hang, ZHAO Huichang, et al. Parameter extraction technique based on periodic ambiguity for PRCPMSFM compound fuze signal［J］. Acta Armamentarii, 2014, 35(5): 627633.
［2］朱航, 张淑宁, 赵惠昌. 基于广义周期性的单通道多分量正弦调频信号分离和参数估计［J］. 电子与信息学报, 2014, 36(10): 24382444.
ZHU Hang, ZHANG Shuning, ZHAO Huichang. Single channel source separation and parameters estimation of multicomponent sinusoid frequency modulation signal based on generalized periodic［J］. Journal of Electronics and Information Technology, 2014, 36(10): 24382444.
［3］尹洪伟, 李国林, 路翠华. 一种基于复值盲分离的欺骗干扰抑制算法［J］. 上海交通大学学报, 2015, 49(10): 15641569.
YIN Hongwei, LI Guolin, LU Cuihua. An algorithm of deception jamming suppression based on complexvalue blind source seperation［J］. Journal of Shanghai Jiao Tong University, 2015, 49(10): 15641569.
［4］朱航, 张淑宁, 赵惠昌. 基于改进自适应分解法的单通道雷达引信混合信号分离［J］. 物理学报, 2014, 63(5): 058401.
ZHU Hang, ZHANG Shuning, ZHAO Huichang. Singlechannel source separation of radar fuze mixed signal using advanced adaptive decomposition［J］. Acta Physica Sinica, 2014, 63(5): 058401.
［5］练秋生, 石保顺, 陈书贞. 字典学习模型、算法及其应用研究进展［J］. 自动化学报, 2015, 41(2): 240260.
LIAN Qiusheng, SHI Baoshun, CHEN Shuzhen. Research advance on dictionary learning models, algorithms and applications［J］. Acta Automatica Sinica, 2015, 41(2): 240260.
［6］MALLAT S G, ZHANG Z. Matching pursuit with timefrequency dictionaries［J］. IEEE Transactions on Signal Processing, 1993, 41(12): 33973415.
［7］王希勤, 刘婧瑶, 孟华东, 等. 一种基于时频原子特征的雷达辐射源信号识别方法［J］. 红外与毫米波学报, 2011, 30(6): 566570.
WANG Xiqin, LIU Jingyao, MENG Huadong, et al. A method for radar emitter signal recognition based on timefrequency atom features［J］. Journal of Infrared Millim Waves, 2011, 30(6): 566570.
［8］BULTAN A. A fourparameter atomic decomposition of chirplets［J］. IEEE Transaction on Signal Processing, 1999, 47(3): 731745.
［9］MENG Q J, SUN N L. A comparison study on gabor, chirplet, FMmlet atom databases for ECG signal processing［C］∥Proceedings of International Conference on Bioinformatics and Biomedical Engineering. Beijing: IEEE, 2009: 13．
［10］GHOFRANI S, AYATOLLAHI A, MCLERNON D C. A comparison of different timefrequency atoms in both ambiguity and timefrequency domains［C］∥Proceedings of the 3rd International Symposium on Image and Signal Processing and Analysis. Rome: IEEE, 2003: 158161.
［11］LOBO A P, LOIZOU P C. Voiced/unvoiced speech discrimination in noise using gabor atomic decomposition［C］∥International Conference on Acoustics, Speech, and Signal Processing. HongKong: IEEE, 2003: 820828.
［12］朱明, 金炜东, 普运伟, 等. 基于Chirplet原子的雷达辐射源信号特征提取［J］. 红外与毫米波学报, 2007, 26(4): 241245.
ZHU Ming, JIN Weidong, PU Yunwei, et al. Feature extraction of radar emitter signals based on gaussian chirplet atoms［J］. Journal of Infrared Millimeter Waves, 2007, 26(4): 241245.
［13］WANG Y, WANG Z, ZHAO B, et al. Compensation for highfrequency vibration of platform in SAR imaging based on adaptive chirplet decomposition［J］. IEEE Geoscience and Remote Sensing Letters, 2016, 13(6): 792795.
［14］WILEY R G. ELINT: The interception and analysis of radar signals［M］. MA: Artech House radar library, 2006.
Options
文章导航

/

〈 〉