基于稀疏贝叶斯的多跳频信号二维波达方向估计

doi:10.16183/j.cnki.jsjtu.2020.04.004

上海交通大学学报 ›› 2020, Vol. 54 ›› Issue (4): 359-368.doi: 10.16183/j.cnki.jsjtu.2020.04.004

基于稀疏贝叶斯的多跳频信号二维波达方向估计

李红光1，郭英1，眭萍1，蔡斌1, 2，苏令华1

1. 空军工程大学信息与导航学院，西安 710077； 2. 北京信息技术研究所，北京 10094

出版日期:2020-04-28 发布日期:2020-04-30
通讯作者: 郭英，女，教授，博士生导师，电话（Tel.）：13379265196；E-mail：kdydsp@163.com.
作者简介:李红光(1986-)，男，天津市人，博士生，从事信息对抗理论研究.
基金资助:
国家自然科学基金(61871396)，军队研究生课题(JY2018C169)资助项目

Two-Dimensional DOA Estimation for Multi-Hopping Signals Based on Sparse Bayes

LI Hongguang 1，GUO Ying 1，SUI Ping 1，CAI Bin 1,2，SU Linghua 1

1. Institute of Information and Navigation, Air Force Engineering University, Xi’an 710077, China； 2. Beijing Information Technology Research Institution, Beijing 10094, China

Online:2020-04-28 Published:2020-04-30

摘要/Abstract

摘要： 分析了现有跳频信号二维波达方向(DOA)估计算法的优缺点，提出了一种基于稀疏贝叶斯学习的跳频信号二维DOA估计算法.该算法利用L型阵列特点，将方位角、俯仰角和跳频率三维信息转换为一维空间频率信息，降低了冗余字典长度和稀疏求解难度.其次，经过奇异值分解降维处理，减少了矩阵运算维数，降低了算法复杂度，通过稀疏贝叶斯算法和快速傅里叶变换估计出空间频率和跳频率，利用Capon空间频率配对算法将空间频率和跳频率正确配对，计算出空间角.最后，由空间角几何关系解算出方位角和俯仰角.模拟结果表明，在低信噪比或低快拍数条件下，该算法DOA估计精度较高，且不易受空间频率间隔和跳频信号源相干性的影响.

关键词: 稀疏贝叶斯, 跳频, 波达方向, 奇异值分解

Abstract: The advantages and disadvantages of the existing two-dimensional direction of arrival (DOA) estimation algorithm for frequency hopping signals are analyzed. A two-dimensional DOA estimation algorithm for frequency hopping signals based on sparse Bayesian learning is proposed. The algorithm uses the characteristics of the L-shaped array to convert the three-dimensional information of azimuth, elevation and hopping frequency into one-dimensional spatial frequency information, which reduces the length of redundant dictionary and the difficulty of sparse solution. Then, after singular value decomposition, the matrix operation dimension is reduced, and the algorithm complexity is reduced. The spatial frequency and the hopping frequency are estimated by the sparse bayes algorithm and the fast fourier transform. The spatial frequency and the hopping frequency are correctly paired by the capon spatial frequency matching algorithm to calculate the spatial angle. Finally, the azimuth and elevation angles are calculated according to the spatial angular relationship. The simulation results show that the DOA estimation performance of the algorithm is good under low signal noise ratio or low fast beat, and it is not easy to be affected by the spatial frequency interval and the coherence of the hopping signal source.

Key words: sparse bayes, frequency hopping, direction of arrival (DDA), singular value decomposition

中图分类号:

TN 911.7

李红光, 郭英, 眭萍, 蔡斌, 苏令华. 基于稀疏贝叶斯的多跳频信号二维波达方向估计[J]. 上海交通大学学报, 2020, 54(4): 359-368.

LI Hongguang, GUO Ying, SUI Ping, CAI Bin, SU Linghua. Two-Dimensional DOA Estimation for Multi-Hopping Signals Based on Sparse Bayes[J]. Journal of Shanghai Jiao Tong University, 2020, 54(4): 359-368.

参考文献

［1］YULAN G, YUE X, MINGMING W, et al. Game-theory-based anti-jamming strategies for frequency hopping wireless communications［J］. IEEE Transactions on Wireless Communications, 2018, 17(8): 5314-5326. ［2］El-AWAMRY A, FAWKY A, KHALIEL M, et al. A novel adaptive spectrum scanning technique for reducing the identification time of the UWB chipless RFID system［C］//IEEE International Conference on Networking. Calabria, Italy: IEEE, 2017: 356-361. ［3］LIANG L, CHENG W, ZHANG W, et al. Mode hopping for anti-Jamming in radio vortex wireless communications［J］. IEEE Transactions on Vehicular Technology, 2018, 67(8): 7018-7032. ［4］LIN C H, FANG W H. Joint angle and delay estimation in frequency hopping systems［J］. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(2): 1042-1056. ［5］陈利虎. 基于空时频分析的多分量跳频信号DOA估计［J］. 系统工程与电子技术, 2011, 33(12): 2587-2592. CHEN Lihu. Directions of arrival estimation for multicomponent frequency-hopping signals based on spatial time-frequency analysis ［J］. Systems Engineering and Electronics, 2011, 33(12): 2587-2592. ［6］张东伟, 郭英, 齐子森, 等. 多跳频信号波达方向与极化状态联合估计算法［J］. 电子与信息学报, 2015, 37(7): 1695-1701. ZHANG Dongwei, GUO Ying, QI Zisen, et al. Joint estimation algorithm of direction of arrival and polarization for multiple frequency-hopping signals［J］. Journal of Electronics and Information Technology, 2015, 37(7): 1695-1701. ［7］FU W H, HEI Y Q, LI X H. UBSS and blind parameters estimation algorithms for synchronous orthogonal FH signals［J］. Journal of Systems Engineering and Electronics, 2014, 25(6): 911-920. ［8］ZHANG C, WANG Y, JING F. Underdetermined blind source separation of synchronous orthogonal frequency hopping signals based on single source points detection［J］. Sensors, 2017, 17(9): 2074-2094. ［9］于欣永, 郭英, 张坤峰, 等. 高效的多跳频信号2D-DOA估计算法［J］. 系统工程与电子技术, 2018, 465(6): 180-187. YU Xinyong, GUO Ying, ZHANG Kunfeng, et al. Efficient 2D-DOA estimation algorithm for multi-FH signals［J］. Systems Engineering and Electronics, 2018, 465(6): 180-187. ［10］张东伟, 郭英, 张坤峰, 等. 多跳频信号频率跟踪与二维波达方向实时估计算法［J］. 电子与信息学报, 2016, 38(9): 2377-2384. ZHANG Dongwei, GUO Ying, ZHANG Kunfeng, et al. Online estimation algorithm of 2D-DOA and frequency tracking for multiple frequency-hopping signals［J］. Journal of Electronics and Information Technology, 2016, 38(9): 2377-2384. ［11］张东伟, 郭英, 齐子森, 等. 跳频信号2D-DOA与极化参数的欠定估计［J］. 哈尔滨工业大学学报, 2016, 48(4): 121-128. ZHANG Dongwei, GUO Ying, QI Zisen, et al. Underdetermined estimation of 2D-DOA and polarization for frequency hopping signals［J］. Journal of Harbin Institute of Technology, 2016, 48(4): 121-128. ［12］CHEN T, WU H, ZHAO Z. The real-valued sparse direction of arrival (DOA) estimation based on the khatri-rao product［J］. Sensors, 2016, 16(5): 693-709. ［13］DAS A. Theoretical and experimental comparison of off-grid sparse Bayesian direction-of-arrival estimation algorithms［J］. IEEE Access, 2017, 5: 18075-18087. ［14］韩树楠, 李东生, 张浩, 等. 基于加权1范数的稀疏重构波达方向估计算法［J］. 探测与控制学报, 2015, 37(2): 82-85. HAN Shunan, LI Dongsheng, ZHANG Hao, et al. A DOA estimation algorithm based on weighted 1 norm through sparse reconstruction［J］. Journal of Detection and Control, 2015, 37(2): 82-85. ［15］LEI W, CHEN B. High-resolution DOA estimation for closely spaced correlated signals using unitary sparse Bayesian learning［J］. Electronics Letters, 2015, 51(3): 285-287. ［16］HUANG Q, ZHANG G, FANG Y. Real-valued DOA estimation for spherical arrays using sparse Bayesian learning［J］. Signal Processing, 2016, 125: 79-86. ［17］CAPON J. High-resolution frequency-wavenumber spectrum analysis［J］. Proceedings of the IEEE, 2005, 57(8): 1408-1418. ［18］QIU S, SHENG W, ZHU L, et al. A robust adaptive beamformer based on low-rank property of steering matrix［C］//IEEE International Symposium on Phased Array Systems and Technology. Waltham, MA, USA: IEEE, 2017: 1-5. ［19］YAN F G, SHUAI L, WANG J, et al. Real-valued root-MUSIC for DOA estimation with reduced-dimension EVD/SVD computation［J］. Signal Processing, 2018, 152: 1-12. ［20］WANG X P, WANG W, LI X, et al. Real-valued covariance vector sparsity-inducing DOA estimation for monostatic MIMO radar［J］. Sensors, 2015, 15(11): 28271-28286.

[1]	魏爽，李文瑶，苏颖，刘睿. 基于偏移全网格的离格稀疏贝叶斯推理的密集时延估计研究[J]. J Shanghai Jiaotong Univ Sci, 2023, 28(6): 763-771.
[2]	李婕, 司伟建, 刘佳宁. 非均匀噪声下的嵌套阵列DOA估计[J]. 空天防御, 2023, 6(4): 86-92.
[3]	李林斌. 深水结构设计流剖面的推算方法研究[J]. 海洋工程装备与技术, 2018, 5(增刊): 193-198.
[4]	吴晨曦，张旻，王可人. 基于连续稀疏重构的宽频段欠定波达方向估计[J]. 上海交通大学学报, 2017, 51(9): 1131-1137.
[5]	周锦瑜, 陈务军, 杜斌, 高成军. 预张力索杆体系分布式静不定与动不定分析[J]. 上海交通大学学报, 2016, 50(03): 345-350.
[6]	毛维平, 李国林, 谢鑫. 未知互耦条件下相干与非相干混合信号的波达方向估计[J]. 上海交通大学学报, 2015, 49(10): 1558-1563.
[7]	高敬礼1，文成林2，刘妹琴1. 基于奇异值分解和叠加法的慢速小目标检测算法[J]. 上海交通大学学报（自然版）, 2015, 49(06): 876-883.
[8]	李艳峰，王新晴，张梅军，朱会杰. 基于奇异值分解和深度信度网络多分类器的滚动轴承故障诊断方法[J]. 上海交通大学学报（自然版）, 2015, 49(05): 681-686.
[9]	闫云斌，王永川，田庆民，李永科. 瑞利衰落信道下噪声跟踪干扰检测性能分析[J]. 上海交通大学学报（自然版）, 2014, 48(10): 1384-1388.
[10]	毛维平，李国林，谢鑫. 单快拍数据预处理的相干信号波达方向估计[J]. 上海交通大学学报（自然版）, 2014, 48(10): 1362-1367.
[11]	赵学智, 叶邦彦. 分量形成方式对奇异值分解信号处理效果的影响[J]. 上海交通大学学报（自然版）, 2011, 45(03): 368-0374.

基于稀疏贝叶斯的多跳频信号二维波达方向估计

Two-Dimensional DOA Estimation for Multi-Hopping Signals Based on Sparse Bayes

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 11

编辑推荐

Metrics

本文评价