基于自适应步长萤火虫-多重信号分类算法的低空目标波达方向估计

周豪; 胡国平; 汪云

doi:10.12000/JR14142

基于自适应步长萤火虫-多重信号分类算法的低空目标波达方向估计

DOI: 10.12000/JR14142

(空军工程大学防空反导学院西安 710051)

基金项目:

国家自然科学基金(61372166)和陕西省自然科学基础研究计划(2014JM8308)资助课题

详细信息

作者简介:
周豪(1990-),男,河南南阳人,硕士研究生在读,主要研究方向为低空目标探测.E-mail:15339216717@163.com胡国平(1964-),男,江西南昌人,教授,博士生导师,主要研究方向为雷达信号处理.E-mail:hgp9162@163.com汪云(1989-),男,江西上饶人,博士研究生在读,主要研究方向为低空目标探测.E-mail:yunyingxikuang@163.com

通讯作者:
周豪15339216717@163.com

计量
- 文章访问数: 2453
- HTML全文浏览量: 374
- PDF下载量: 1303
- 被引次数: 0
出版历程
- 收稿日期: 2014-11-25
- 修回日期: 2015-04-28
- 网络出版日期: 2015-06-28

DOA Estimation of Low Altitude Target Based on Adaptive Step Glowworm Swarm Optimization-multiple Signal Classification Algorithm

Air and Missile Defense College, Air Force Engineering University, Xi'an 710051, China

摘要

摘要: 该文针对传统多重信号分类算法(MUSIC)不适用于低空多径环境下目标波达方向(DOA)估计且谱峰搜索计算量大的问题,在解相干基础上,提出一种基于自适应步长萤火虫算法的多重信号分类算法.该方法通过对快拍数据协方差矩阵虚拟平滑实现多径信号的完全解相干和满秩相关矩阵的构造,利用自适应步长萤火虫算法实现谱峰搜索和目标角度估计.仿真结果表明,新方法能够在无孔径损失的情况下较好克服低空多径效应影响,快速、精确地估计目标波达方向.
- 多重信号分类算法 /
- 解相干 /
- 自适应步长萤火虫算法
Abstract: The traditional MUltiple SIgnal Classification (MUSIC) algorithm requires significant computational effort and can not be employed for the Direction Of Arrival (DOA) estimation of targets in a low-altitude multipath environment. As such, a novel MUSIC approach is proposed on the basis of the algorithm of Adaptive Step Glowworm Swarm Optimization (ASGSO). The virtual spatial smoothing of the matrix formed by each snapshot is used to realize the decorrelation of the multipath signal and the establishment of a fullorder correlation matrix. ASGSO optimizes the function and estimates the elevation of the target. The simulation results suggest that the proposed method can overcome the low altitude multipath effect and estimate the DOA of target readily and precisely without radar effective aperture loss.
- MUSIC algorithm /
- De-correlation /
- Adaptive Step Glowworm Swarm Optimization (ASGSO)

HTML全文

参考文献(15)

[1]	杨雪亚, 陈伯孝, 赵光辉, 等. 基于二维空间平滑的波束域 MUSIC算法[J]. 系统工程与电子技术, 2010, 32(5): 895-899. Yang Xue-ya, Chen Bai-xiao, Zhao Guang-hui, et al.. Beamspace MUSI Cmethod based on 2D spatial smoothing[J]. Systems Engineering and Electronic, 2010, 32(5): 895-899.
[2]	童宁宁, 郭艺夺, 王光明. 米波雷达低角跟踪环境下的修正 MUSIC算法[J]. 现代雷达, 2008, 30(10): 29-32. Tong Ning-ning, Guo Yi-duo, and Wang Guang-ming. Modified MUSIC algorithm in meter-band radar low-angle tracking environment[J]. Modern Radar, 2008, 30(10): 29-32.
[3]	朱圣棋, 廖桂生, 李海, 等. 基于数据矩阵的非圆相干信号完全解相干算法[J]. 系统工程与电子技术, 2009, 31(1): 21-24. Zhu Sheng-qi, Liao Gui-sheng, Li Hai, et al.. DOA estimation of coherent signals based on data matrix[J]. Systems Engineering and Electronics, 2009, 31(1): 21-24.
[4]	侯姗姗, 谢庆, 廖峰, 等. 基于超声阵列传感器与遗传 MUSIC的局放源波达方向估计[J]. 电测与仪表, 2014, 51(5): 11-14. Hou Shan-shan, Xie Qing, Liao Feng, et al.. Source DOA estimation based on ultrasonic array sensor and Genetic MUSIC[J]. Electrical Measurement Instrumentation, 2014, 51(5): 11-14.
[5]	赵远东, 汪怡. 基于均分法的小生境遗传算法[J]. 南京信息工程大学学报(自然科学版), 2013, 5(6): 553-556. Zhao Yuan-dong and Wang Yi. Niche genetic algorithm research based on average method[J]. Journal of Nanjing University of Information Science and Technology: Natural Science Edition, 2013, 5(6): 553-556.
[6]	陈彦龙, 张培林, 李胜, 等. 面向多峰函数的自适应小生境量子进化算法[J]. 系统工程与电子技术, 2014, 36(2): 403-408. Chen Yan-long, Zhang Pei-lin, Li Sheng, et al.. Adaptive niche quantum evolutionary algorithm for multimodal function[J]. Systems Engineering and Electronic, 2014, 36(2): 403-408.
[7]	欧阳喆, 周永权. 自适应步长萤火虫优化算法[J]. 计算机应用, 2011, 31(7): 1804-1807. Ouyang Zhe and Zhou Yong-quan. Self-adaptive step glowworm swarm optimization algorithm[J]. Journal of Computer Applications, 2011, 31(7): 1804-1807.
[8]	王凌, 李国林, 谢鑫, 等. 非圆信号二维DOA和初始相位联合估计方法[J]. 雷达学报, 2012, 1(1): 43-49. Wang Ling, Li Guo-lin, Xie Xin, et al.. Joint 2-D DOA and noncircularity phase estimation method[J]. Journal of Radars, 2012, 1(1): 43-49.
[9]	张瑜, 李玲玲. 低角雷达跟踪时的多路径散射模型[J]. 电波科学学报, 2004, 19(1): 83-86. Zhang Yu and Li Ling-ling. Multipath scatting model of low angle radar tracking[J]. Chinese Journal of Radio Science, 2004, 19(1): 83-86.
[10]	Krishnand K N and Ghose D. Detection of multiple source locations using a glowworm metaphor with applications to collective robotics[C]. Swarm Intelligence Symposium, Washington. D.C, USA, June 2005: 84-91.
[11]	黄正新. 人工萤火虫群优化算法分析改进及应用研究[D]. [硕士论文], 广西民族大学, 2011: 6-7. Huang Zheng-xin. Research on artificial Glowworm Swarm Optimization algorithm analysis improve and application[D]. [Master dissertation], Guangxi University for Nationalities, 2011: 6-7.
[12]	吴伟民, 亢少将, 林志毅, 等. 基于改进萤火虫算法的多模函数优化[J]. 计算机应用与软件, 2014, 31(1): 283-285, 302. Wu Wei-min, Kang Shao-jiang, Lin Zhi-yi, et al.. Multimodal function optimization based on improved Glowworm Swarm Optimization[J]. Computer Applications and Software, 2014, 31(1): 283-285, 302.
[13]	李逦, 姚晔, 李铁. 基于改进型人工萤火虫算法的云计算资源研究[J]. 计算机应用研究, 2013, 30(8): 2298-2300, 2333. Li Li, Yao Ye, and Li Tie. Study on improved artificial firefly algorithm in cloud computing resources[J]. Application Research of Computers, 2013, 30(8): 2298-2300, 2333.
[14]	Krishnand K N and Ghose D. Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions[C]. Swarm Intelligence Symposium, Boston, USA, 2009: 87-124.
[15]	张兴良, 王可人, 樊甫华. 典型阵列快速MUSIC算法研究[J]. 雷达学报, 2012, 1(2): 149-156. Zhang Xing-liang, Wang Ke-ren, and Fan Fu-hua. Study on fast MUSIC algorithm with typical array[J]. Journal of Radars, 2012, 1(2): 149-156.