非圆信号的贝叶斯稀疏重构阵列测向方法

doi:10.7527/S1000-6893.2013.0461

电子与控制

本期目录 | 过刊浏览 | 高级检索

前一篇 | 后一篇

非圆信号的贝叶斯稀疏重构阵列测向方法

刘章孟^1,2, 周一宇², 吴海斌³

1. 电子信息系统复杂电磁环境效应国家重点实验室, 河南洛阳 471003;
2. 国防科学技术大学电子科学与工程学院, 湖南长沙 410073;
3. 总参四部驻桂林地区军代室, 广西桂林 541001

收稿日期:2013-04-27 修回日期:2013-11-05 出版日期:2014-03-25 发布日期:2013-11-29
通讯作者: 刘章孟，Tel.：0731-84573490 E-mail：liuzhangmeng@nudt.edu.cn E-mail:liuzhangmeng@nudt.edu.cn
作者简介:刘章孟男，博士，讲师。主要研究方向：阵列信号处理、稀疏重构。Tel：0731-84573490 E-mail：liuzhangmeng@nudt.edu.cn；周一宇男，博士，教授，博士生导师。主要研究方向：综合电子战系统与技术。E-mail：zhouyiyu@sohu.com
基金资助:
国家自然科学基金（61302141）；电子信息系统复杂电磁环境效应国家重点实验室主任基金（CEMEE2014Z0202B）

Direction of Arrival Estimation Method of Non-circular Signals via Sparse Bayesian Reconstruction

LIU Zhangmeng^1,2, ZHOU Yiyu², WU Haibin³

1. State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, Luoyang 471003, China;
2. School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China;
3. Guilin Military Representative Bureau, The 4th Department of PLA General Staff Headquarters, Guilin 541001, China

Received:2013-04-27 Revised:2013-11-05 Online:2014-03-25 Published:2013-11-29
Supported by:
National Natural Science Foundation of China (61302141); Director Foundation of CEMEE (CEMEE2014Z0202B)

摘要/Abstract

摘要：

对信号非圆特性的有效利用能显著改善子空间类阵列测向方法的性能，但难以弥补此类方法在低信噪比（SNR）、小样本等信号环境适应能力方面的局限。本文引入贝叶斯稀疏学习（SBL）技术以解决非圆信号的波达方向（DOA）估计问题，在结合信号非圆特性的同时对入射信号的空域稀疏性加以利用，通过将非圆信号阵列输出协方差矩阵和共轭协方差矩阵在预先定义的空域字典集上进行稀疏重构，得到入射信号的空间谱重构结果，并依据其谱峰位置估计各信号的方向。该方法对独立和相关信号都具有较好的适应能力，仿真结果验证了该方法在信号环境适应能力和相关信号测向精度等方面的优势。

关键词: 阵列处理, 波达方向估计, 非圆信号, 贝叶斯稀疏学习, 联合稀疏重构, 相干信号

Abstract:

The performance of the subspace-based direction of arrival (DOA) estimation methods can be improved significantly via effective exploitation of the non-circularity of the incident signals, but the shortcomings of these methods in adaptation to demanding scenarios, such as low signal-to-noise ratio (SNR) and limited snapshots, can hardly be made up. The sparse Bayesian learning (SBL) technique is introduced in this paper to deal with the DOA estimation problem of non-circular signals. The spatial sparsity of the incident signals is exploited together with their non-circularity property, and the covariance and conjugate covariance matrices of the array outputs of non-circular signals are decomposed jointly under a sparsity constraint to reconstruct the spatial spectrum of the incident signals, and the DOA estimates are finally obtained according to the spectrum peak locations. This method is robust against inter-signal correlation, and its superiorities in adaptation to demanding scenarios as well as in DOA estimation precision are demonstrated by the simulation results.

Key words: array processing, direction of arrival estimation, non-circular signal, sparse Bayesian learning, joint sparse reconstruction, coherent signal

中图分类号:

刘章孟, 周一宇, 吴海斌. 非圆信号的贝叶斯稀疏重构阵列测向方法[J]. 航空学报, 2014, 35(3): 821-827.

LIU Zhangmeng, ZHOU Yiyu, WU Haibin. Direction of Arrival Estimation Method of Non-circular Signals via Sparse Bayesian Reconstruction[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2014, 35(3): 821-827.

参考文献

[1] Krim H, Viberg M. Two decades of array signal processing research: the parametric approach[J]. IEEE Signal Processing Magazine, 1996, 13(4): 67-94.

[2] Schmidt R. Multiple emitter location and signal parameter estimation[J]. IEEE Transactions on Antennas and Propagation, 1986, 34(3): 276-280.

[3] Edelblute D J. Noncircularity[J]. IEEE Signal Processing Letters, 1996, 3(5): 156-157.

[4] Gounon P, Adnet C, Galy J. Localization angulaire de signaux non circulaires[J]. Traitement du Signal, 1998, 15(1): 17-23.

[5] Charge P, Wang Y, Saillard J. A non-circular sources direction finding method using polynomial rooting[J]. Signal Processing, 2001, 81(8): 1765-1770.

[6] Abeida H, Delmas J P. MUSIC-like estimation of direction of arrival for noncircular sources[J]. IEEE Transactions on Signal Processing, 2006, 54(7): 2678-2690.

[7] Liu J, Huang Z T, Zhou Y. Extended 2q-MUSIC algorithm for noncircular signals[J]. Signal Processing, 2008, 88(6): 1327-1339.

[8] Gao F, Nallanathan A, Wang Y. Improved MUSIC under the coexistence of both circular and noncircular sources[J]. IEEE Transactions on Signal Processing, 2008, 56(7): 3033-3038.

[9] Gorodnitsky I F, Rao B D. Sparse signal reconstruction from limited data using FOCUSS: A re-weighted minimum norm algorithm[J]. IEEE Transactions on Signal Processing, 1997, 45(3): 600-616.

[10] Fuchs J J. On the application of the global matched filter to DOA estimation with uniform circular arrays[J]. IEEE Transactions on Signal Processing, 2001, 49(4): 702-709.

[11] Malioutov D, Cetin M, Willsky A S. A sparse signal reconstruction perspective for source localization with sensor arrays[J]. IEEE Transactions on Signal Processing, 2005, 53(8): 3010-3022.

[12] Liu Z M, Huang Z T, Zhou Y Y. Direction-of-arrival estimation of wideband signals via covariance matrix sparse representation[J]. IEEE Transactions on Signal Processing, 2011, 59(9): 4256-4270.

[13] Yin J, Chen T. Direction-of-arrival estimation using a sparse representation of array covariance vectors[J]. IEEE Transactions on Signal Processing, 2011, 59(9): 4489-4493.

[14] Liu Z M, Huang Z T, Zhou Y Y. An efficient maximum likelihood method for direction-of-arrival estimation via sparse Bayesian learning[J]. IEEE Transactions on Wireless Communications, 2012, 11(10): 3607-3617.

[15] Liu Z M, Huang Z T, Zhou Y Y. Sparsity-inducing direction finding for narrowband and wideband signals based on array covariance vectors[J]. IEEE Transactions on Wireless Communications, 2013, 12(8): 3896-3907.

[16] Liu Z M, Zhou Y Y. A unified framework and sparse Bayesian perspective for direction-of-arrival estimation in the presence of array imperfections[J]. IEEE Transactions on Signal Processing, 2013, 61(15): 3786-3798.

[17] Tipping M E. Sparse Bayesian learning and the relevance vector machine[J]. Journal of Machine Learning Research, 2001, 1(1): 211-244.

[18] Wipf D P. Bayesian methods for finding sparse representations[D]. San Diego: University of California, 2006.

[19] Dempster A P, Laird N M, Rubin D B. Maximum likelihood from incomplete data via the EM algorithm[J]. Journal of the Royal Statistical Society Series B-Statistical Methodology, 1977, 39(1): 1-38.

[20] Stoica P, Selen Y. Model-order selection: A review of information criterion rules[J]. IEEE Signal Processing Magazine, 2004, 21(4): 36-47.

[21] Fuchs J J. Multipath time-delay detection and estimation[J]. IEEE Transactions on Signal Processing, 1999, 47(1): 237-243.

E-mail：hkxb@buaa.edu.cn

关于我们

期刊社服务

专业学科

封面文章

友情链接

主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

非圆信号的贝叶斯稀疏重构阵列测向方法

Direction of Arrival Estimation Method of Non-circular Signals via Sparse Bayesian Reconstruction

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 14

编辑推荐

Metrics

本文评价

[1]	刘帅琦, 王布宏, 李夏, 刘新波, 曹帅. 二维嵌套混合MIMO相控阵雷达接收阵列设计[J]. 航空学报, 2018, 39(4): 321546-321546.
[2]	代正亮, 巴斌, 张彦奎, 崔维嘉, 王大鸣. 回溯降维相干分布式非圆信号DOA快速估计[J]. 航空学报, 2017, 38(9): 321034-321034.
[3]	郭婷婷, 徐友根. 非圆特征恢复特征空间投影鲁棒波束形成[J]. 航空学报, 2016, 37(9): 2833-2838.
[4]	刘雄厚, 孙超, 卓颉, 马茜, 潘浩. 一种用于高分辨扇扫成像的MIMO阵列[J]. 航空学报, 2014, 35(9): 2540-2550.
[5]	李婧, 曾操, 廖桂生, 陶海红, 赵元楠. 抗主瓣干扰的平移不变子阵干涉测角方法[J]. 航空学报, 2013, 34(12): 2804-2814.
[6]	王克让, 何劲, 贺亚鹏, 朱晓华, 张燕洪. MIMO雷达DOD和DOA联合估计算法: RTR-ESPRIT [J]. 航空学报, 2012, (5): 893-901.
[7]	罗争, 张旻, 李鹏飞. 基于协方差矩阵高阶幂的二维DOA估计新算法[J]. 航空学报, 2012, (4): 696-704.
[8]	潘捷, 周建江, 汪飞. 非均匀噪声稀疏均匀圆阵的二维DOA估计[J]. 航空学报, 2011, 32(3): 448-456.
[9]	刘兆霆;刘中;何劲. 线性极化敏感阵列的极化平滑算法及相干源参数估计[J]. 航空学报, 2010, 31(8): 1646-1652.
[10]	江胜利;王鞠庭;何劲;刘中. 冲击杂波下的MIMO雷达DOA估计方法[J]. 航空学报, 2009, 30(8): 1454-1459.
[11]	游鸿;黄建国. 子空间投影DOA估计算法分析及合成空间谱[J]. 航空学报, 2008, 29(5): 1334-1339.
[12]	夏铁骑;万群;汪学刚. 冲击噪声环境下基于任意阵列流形的空时二维DOA估计方法[J]. 航空学报, 2008, 29(5): 1233-1238.
[13]	陈建峰;黄建国. 阵列高分辨方位估计方法统计性能分析[J]. 航空学报, 1998, 19(1): 114-117.
[14]	张铭;朱兆达. 实数域中的投影法及其在单次快摄阵处理中的应用[J]. 航空学报, 1994, 15(11): 1334-1340.