激光与光电子学进展, 2018, 55 (8): 080702, 网络出版: 2018-08-13  

基于混合遗传鸡群优化算法的雷达正交波形设计 下载: 591次

Design of Radar Orthogonal Waveform Based on Hybrid Genetic Chicken Swarm Optimization Algorithm
作者单位
江南大学物联网工程学院, 江苏 无锡 214122
摘要
针对复杂战场环境下同型号舰载雷达发射信号之间容易出现同频干扰的问题,设计出具有正交特性的调频编码信号。对常规的正交波形编码的搜索方向进行改进,利用混合遗传鸡群算法找出具有低自相关特性和低互相关特性的编码序列。该算法利用反向学习的鸡群算法进行搜索寻优,引入学习因子和遗传算法中的变异和交叉思想对个体进行更新迭代。在适应度函数中引入集对分析联系度综合评价,根据集对分析联系度来引导算法的搜索方向,得到具有更好正交特性的调频编码序列脉冲信号。对得到的雷达信号的模糊函数、回波信号的匹配滤波情况以及不同雷达数量下仿真信号的正交性分别进行仿真,仿真结果验证了所设计的调频编码信号能达到抗同频干扰的目的。
Abstract
Considering the problem that the same frequency interference is easy to occur between the radar signals transmitted by the shipborne radar with same type in the complex battlefield environment, we design a frequency modulation (FM) coded signal with orthogonal characteristics. The search direction of conventional waveform coding with the orthogonal characteristic is improved, and the hybrid genetic chicken swarm optimization (HGCSO) is used to identify the coding sequences with low autocorrelation and low cross correlation characteristics. We adopt the back-learning chicken swarm algorithm to search the optimal values, and introduce the learning factor and the idea of mutation and crossover in genetic algorithm to update the individuals. A comprehensive evaluation of set pair analysis is used in fitness function, and the search direction is guided according to the relational degree of setting pair analysis to get a FM coding sequence pulse signal with excellent orthogonality. The fuzzy function of the obtained radar signal, the matched filtering of the echo signal, and the orthogonality of the simulation signal at different radar numbers are simulated respectively, and the results of simulations show that the FM coded signal designed by this algorithm can effectively resist the same frequency interference.
参考文献

[1] 郇浩, 陶然, 李元硕, 等. 基于变换域和时域联合处理的雷达同频干扰抑制方法[J]. 电子与信息学报, 2012, 34(12): 2978-2984.

    Huan H, Tao R, Li Y S, et al. Co-channel interference suppression for homo-type radars based on joint transform domain and time domain[J]. Journal of Electronics & Information Technology, 2012, 34(12): 2978-2984.

[2] 赵国庆. 雷达对抗原理[M]. 西安: 西安电子科技大学出版社, 1999.

    Zhao G Q. The principle of radar countermeasure[M]. Xi′an: Xidian University Press, 1999.

[3] 薛春祥. 编队情况下舰载雷达抗同频干扰方法研究[D]. 南京: 东南大学, 2007.

    Xue C X. Research on shipboard radar anti-same-frequency interference method in formation[D]. Nanjing: Southeast University, 2007.

[4] 高航, 薛凌云. 基于改进遗传算法的反向传播神经网络拟合LED光谱模型[J]. 激光与光电子学进展, 2017, 54(7): 072302.

    Gao H, Xue L Y. Back propagation neural network based on improved genetic algorithm fitting LED spectral model[J]. Laser & Optoelectronics Progress, 2017, 54(7): 072302.

[5] 刘剑飞, 王少影, 曾祥烨, 等. 基于群智能算法的光OFDM系统PAPR抑制[J]. 光学学报, 2017, 37(1): 0106006.

    Liu J F, Wang S Y, Zeng X Y, et al. PAPR reduction in optical OFDM systems based on swarm intelligence algorithms[J]. Acta Optica Sinica, 2017, 37(1): 0106006.

[6] 段绿林, 刘东, 张与鹏, 等. 基于混合智能算法的激光雷达数据拼接技术[J]. 光学学报, 2017, 37(6): 0601002.

    Duan L L, Liu D, Zhang Y P, et al. Lidar data gluing technology based on hybrid intelligent algorithm[J]. Acta Optica Sinica, 2017, 37(6): 0601002.

[7] Singh S P, Rao K S. Discrete frequency coded radar signal design[J]. IET Signal Processing, 2009, 3(1): 7-16.

[8] Deng H. Discrete frequency-coding waveform design for netted radar systems[J]. IEEE Signal Processing Letters, 2004, 11(2): 179-182.

[9] 杨进, 邱兆坤, 黎湘, 等. 一种基于混沌序列的随机离散频率编码信号[J]. 电子与信息学报, 2011, 33(11): 2702-2708.

    Yang J, Qiu Z K, Li X, et al. Random discrete frequency coding signal based on chaotic series[J]. Journal of Electronics & Information Technology, 2011, 33(11): 2702-2708.

[10] 王敦勇, 袁俊泉, 马晓岩. 基于遗传算法的MIMO雷达离散频率编码波形设计[J]. 空军雷达学院学报, 2007, 21(2): 105-107.

    Wang D Y, Yuan J Q, Ma X Y. Design of discrete frequency-coding waveforms for MIMO radar via genetic algorithm[J]. Journal of Air Force Radar Academy, 2007, 21(2): 105-107.

[11] 李敬军, 姜永华, 但波. MIMO雷达中正交离散频率编码波形的设计[J]. 信号处理, 2013, 29(9): 1176-1181.

    Li J J, Jiang Y H, Dan B. Orthogonal discrete frequency coding waveforms design in MIMO radar[J]. Journal of Signal Processing, 2013, 29(9): 1176-1181.

[12] Liu B, He Z S, He Q. Optimization of orthogonal discrete frequency-coding waveform based on modified genetic algorithm for MIMO radar[C]. International Conference on Communications, Circuits and Systems, 2007: 966-970.

[13] 周沫, 李汉钊. 雷达调频编码脉冲信号的设计与处理[J]. 海军工程大学学报, 2007, 19(5): 68-72.

    Zhou M, Li H Z. Design and processing of radar frequency modulation coded pulse signal[J]. Journal of Naval University of Engineering, 2007, 19(5): 68-72.

[14] Meng X, Liu Y, Gao X, et al. A new bio-inspired algorithm: chicken swarm optimization[M]∥Tan Y, Shi Y, Coello C A C. Advances in swarm intelligence. Germany: Springer International Publishing, 2014: 86-94.

[15] 魏伟一, 文雅宏. 一种精英反向学习的萤火虫优化算法[J]. 智能系统学报, 2017, 12(5): 710-716.

    Wei W Y, Wen Y H. Firefly optimization algorithm utilizing elite opposition-based learning[J]. CAAI Transactions on Intelligent Systems, 2017, 12(5): 710-716.

[16] 陈贵敏, 贾建援, 韩琪西. 粒子群优化算法的惯性权值递减策略研究[J]. 西安交通大学学报, 2006, 40(1): 53-56.

    Chen G M, Jia J Y, Han Q X. Study on the strategy of decreasing inertia weight in particle swarm optimization algorithm[J]. Journal of Xi′an Jiaotong University, 2006, 40(1): 53-56.

[17] 赵克勤. 基于集对分析的不确定性多属性决策模型与算法[J]. 智能系统学报, 2010, 5(1): 41-50.

    Zhao K Q. Decision making algorithm based on set pair analysis for use when facing multiple uncertain attributes[J]. CAAI Transactions on Intelligent Systems, 2010, 5(1): 41-50.

[18] 魏明华, 郑志宏, 黄强, 等. 基于改进SPA法的地下水环境模糊综合评判[J]. 水利学报, 2009, 40(10): 1204-1209.

    Wei M H, Zheng Z H, Huang Q, et al. Fuzzy comprehensive evaluation of groundwater environment based on improved set pair analysis[J]. Journal of Hydraulic Engineering, 2009, 40(10): 1204-1209.

[19] 邬敏, 李祚泳, 刘智勇, 等. 基于遗传集对分析的空气环境质量评价[J]. 环境科学与技术, 2009, 32(2): 168-171.

    Wu M, Li Z Y, Liu Z Y, et al. Air environment quality assessment based on set pair analysis combined with genetic algorithm[J]. Environmental Science & Technology, 2009, 32(2): 168-171.

[20] Kumar K, Garg H. Connection number of set pair analysis based TOPSIS method on intuitionistic fuzzy sets and their application to decision making[J]. Applied Intelligence, 2017(5): 1-8.

杨俊辉, 刘以安. 基于混合遗传鸡群优化算法的雷达正交波形设计[J]. 激光与光电子学进展, 2018, 55(8): 080702. Yang Junhui, Liu Yian. Design of Radar Orthogonal Waveform Based on Hybrid Genetic Chicken Swarm Optimization Algorithm[J]. Laser & Optoelectronics Progress, 2018, 55(8): 080702.

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