基于重加权l1范数惩罚的远近场混合源定位算法

doi:10.3969/j.issn.0372-2112.2016.10.023

电子学报 ›› 2016, Vol. 44 ›› Issue (10): 2440-2448.DOI: 10.3969/j.issn.0372-2112.2016.10.023

基于重加权l₁范数惩罚的远近场混合源定位算法

田野, 练秋生

燕山大学信息科学与工程学院, 河北秦皇岛 066004

收稿日期:2015-04-22 修回日期:2015-08-17 出版日期:2016-10-25 发布日期:2016-10-25
作者简介:田野,男,1985年12月生于河北省平泉县,2014年于吉林大学通信与信息系统专业获得博士学位,现为燕山大学信息科学与工程学院讲师,主要研究方向为阵列信号处理、稀疏信号重构等.E-mail:tianye@ysu.edu.cn;练秋生,男,1969年8月生于江西遂川,工学博士,现为燕山大学信息科学与工程学院教授、博士生导师,主要研究方向为稀疏表示,压缩感知等.E-mail:lianqs@ysu.edu.cn
基金资助:
国家自然科学基金（No.61471313，No.61601398）；河北省自然科学基金（No.F2016203100，No.F2014203076）；河北省高等学校青年拔尖人才计划（No.BJ2016051）

Mixed Far-Field and Near-Field Source Localization Algorithm Based on Reweighted l₁-Norm Penalty

TIAN Ye, LIAN Qiu-sheng

School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China

Received:2015-04-22 Revised:2015-08-17 Online:2016-10-25 Published:2016-10-25

摘要/Abstract

摘要：

现有信源定位方法大多假定信源是远场源或近场源，而实际定位系统中往往存在远场源和近场源共存的情况.为实现远、近场源分离及高精度信源定位，本文在稀疏信号重构理论框架下提出了一种新的远近场混合源定位算法.该算法利用阵列协方差矩阵反对角线元素和重加权l₁范数惩罚获得所有信源的到达角（Direction Of Arrival，DOA）估计.在DOA估计的基础上，根据远场与近场源距离参数位于不同区间的特点利用一维搜索实现远、近场源分离以及近场源距离参数的估计.从理论角度分析了重加权l₁范数惩罚算法的重构性能.本文所提算法不仅同时适用于高斯和非高斯信号，而且无需多维搜索和参数配对，也无需信源数的先验信息，同时还可以获得较好的定位精度.计算机仿真结果验证了所提算法的有效性.

关键词: 阵列信号处理, 远场源, 近场源, 远近场混合源, 重加权l₁范数惩罚

Abstract:

Existing source localization methods mostly assume that the sources are pure near-field sources or pure far-field sources.While in practical localization systems,both far-field and near-field sources may exist simultaneously.To classify far-field and near-field sources,and also to achieve high-precision source localization,a novel mixed far-field and near-field source localization algorithm is proposed in sparse signal reconstruction framework.The algorithm first utilizes anti-diagonal elements of array covariance matrix and reweighted l₁-norm penalty to obtain DOA estimation of all sources,then classifies far-field and near-field sources and successively obtains range estimation of near-field sources via one-dimensional search,by exploring the feature that the range parameters of far-field and near-field sources are located in different areas.Theoretically,we analyze the reconstruction performance of the reweighted l₁-norm penalty algorithm.The proposed algorithm is not only suitable for dealing with Gaussian signals and non-Gaussian signals,but also without multi-dimensional search and parameter pairing process,and also without knowing the number of sources.Meanwhile,the proposed algorithm can even provide good estimation accuracy.Computer simulation results validate the effectiveness of the proposed algorithm.

Key words: array signal processing, far-field sources, near-field sources, mixed far-field and near-field sources, reweighted l₁-norm penalty

中图分类号:

TN911.7

田野, 练秋生. 基于重加权l₁范数惩罚的远近场混合源定位算法[J]. 电子学报, 2016, 44(10): 2440-2448.

TIAN Ye, LIAN Qiu-sheng. Mixed Far-Field and Near-Field Source Localization Algorithm Based on Reweighted l₁-Norm Penalty[J]. Acta Electronica Sinica, 2016, 44(10): 2440-2448.

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] Schmit R O.Multiple emitter location and signal parameter estimation[J].IEEE Transactions on Antennas and Propagation,1986,34(3):276-280.
[3] Wang Q,Wang L,An K,et al.DOA estimation of smart antenna signal based on MUSIC algorithm[J].Journal of Networks,2014,9(5):1309-1316.
[4] Roy R,Kailath T.ESPRIT-a subspace rotation approach to estimation of parameters of cissoids in noise[J].IEEE Transactions on Acoustic,Speech and Signal Processing,1986,34(10):1340-1342.
[5] Ma Y,Chen B,Yang M,et al.A novel ESPRIT-based algorithm for DOA estimation with distributed subarray antenna[J].Circuits,Systems,and Signal Processing,2015,34(9):1-22.
[6] 王军,闫锋刚,金铭,等.基于噪声子空间映射的二维波达角快速估计算法[J].电子学报,2015,43(2):276-282. Wang J,Yan F G,Jin M,et al.Efficient algorithm for 2-D DOA estimation based on noise subspace mapping[J].Acta Electronica Sinica,2015,43(2):276-282.(in Chinese)
[7] Huang Y D,Barkat M.Near-field multiple source localization by passive sensor array[J].IEEE Transaction on Antennas and Propagation,1991,39(7):968-975.
[8] 梁国龙,韩博.基于互素对称阵的近场源定位[J].电子与信息学报,2014,36(1):135-139. Liang G L,Han B.Near-field sources localization based on co-prime symmetric array[J].Journal of Electronics & Information Technology,2014,36(1):135-139.(in Chinese)
[9] Yuen N,Friedlander B.Performance analysis of high order ESPRIT for localization of near-field sources[J].IEEE Transactions on Signal Processing,1998,46(3):709-719.
[10] Starer D,Nehorai A.Passive localization of near-field sources by path following[J].IEEE Transactions on Signal Processing,1994,42(2):677-680.
[11] Grosicki E,Abed M K,Hua A.A weighted linear prediction method for near-field source localization[J].IEEE Transactions on Signal Processing,2005,53(10):3651-3660.
[12] Cirillo L A,Zoubir A M,Amin M G.Estimation of near-field parameters using spatial time-frequency[A].Proceedings of the IEEE International Conference on Acoustics,Speech and Signal Processing[C].USA:IEEE Press,2007.1141-1144.
[13] Arslan G,Sakarya F A,Evans B L.Speaker localization for far-field and near-field wideband sources using neural networks[A].Proceedings of the IEEE EURASIP Workshop on Nonlinear Signal Image Processing[C].Antalya,Turkey:IEEE Press,1999.528-532.
[14] Mukai R,Sawada H,Araki S,et al.Frequency-domain blind source separation of many speech signals using near-field and far-field models[J].EURASIP Journal on Applied Signal Processing,2006,Article ID 83683:1-13.
[15] Argentieri S,Danes P,Soueres P.Modal analysis based beamforming for nearfield or farfield speaker localization in robotics[A].Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems[C].Beijing,China:IEEE Press,2006.866-871.
[16] Kennedy R A,Ward D B,Thushara P,et al.Nearfield beamforming using nearfield/farfield reciprocity[A].Proceedings of the IEEE International Conference on Acoustics,Speech and Signal Processing[C].USA:IEEE Press,1999.3741-3744.
[17] Liang J L,Liu D.Passive localization of mixed near-field and far-field sources using two-stage MUSIC algorithm[J].IEEE Transaction on Signal Processing,2010,58(1):108-120.
[18] He J,Swamy M N S,Ahmad M O.Efficient application of MUSIC algorithm under the coexistence of far-field and near-field sources[J].IEEE Transaction on Signal Processing,2012,60(4):2066-2070.
[19] Wang B,Zhao Y,Liu J.Mixed-order MUSIC algorithm for localization of far-field and near-field sources[J].IEEE Signal Processing Letters,2013,20(4):311-314.
[20] Candès E J,Wakin M B.An introduction to compressive sensing[J].IEEE Signal Processing Magazine,2008,25(2):21-30.
[21] 王强,李佳,沈毅.压缩感知中确定性测量矩阵构造算法综述[J].电子学报,2013,41(10):2041-2050. Wang Q,Li J,Shen Y.A survey on deterministic measurement matrix construction algorithms in compressive sensing[J].Acta Electronica Sinica,2013,41(10):2041-2050.(in Chinese)
[22] Wang B,Liu J J,Sun X Y.Mixed sources localization based on sparse signal reconstruction[J].IEEE Signal Processing Letters,2012,19(8):487-490.
[23] Donoho D L,Elad M,Temlyakov V N.Stable recovery of sparse overcomplete representations in the presence of noise[J].IEEE Transactions on Information Theory,2006,52(1):6-18.
[24] Zheng C,Li G,Zhang H,et al.An approach of DOA estimation using noise subspace weighted l₁ minimization[A].Proceedings of the IEEE International Conference on Acoustics,Speech and Signal Processing[C].USA:IEEE Press,2011.2856-2859.
[25] Fan J,Li R.Variable selection via nonconcave penalized likelihood and its oracle properties[J].Journal of the American Statistical Association,2001,96(456):1348-1360.
[26] Candès E J,Wakin M B.Enhancing sparsity by reweighted l₁ minimization[J].Journal of Fourier Analysis and Applications,2006,101(476):1418-1429.
[27] Hyder M M,Mahata K.Direction-of-arrival estimation using a mixed l_2,0 norm approximation[J].IEEE Transactions on Signal Processing,2010,58(9):4646-4655.
[28] Shao J.Linear model selection by cross-validation[J].Journal of the American Statistical Association,1993,88(422):486-494.
[29] 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.

基于重加权l₁范数惩罚的远近场混合源定位算法

Mixed Far-Field and Near-Field Source Localization Algorithm Based on Reweighted l₁-Norm Penalty

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	郑恩明, 陈新华, 周权斌, 李嶷, 杨鹤, 孟浩. 一种复域压缩感知目标方位估计方法[J]. 电子学报, 2021, 49(11): 2117-2123.
[2]	刘松, 廖勇, 谢远举. 一种高效稳健的均匀圆阵互耦校正方法[J]. 电子学报, 2017, 45(9): 2170-2176.
[3]	陈鑫, 刘振, 魏玺章. 基于旋转均匀圆阵的单近场源参数估计解模糊算法[J]. 电子学报, 2017, 45(3): 584-590.
[4]	梁国龙, 韩博, 林旺生, 王丹. 基于稀疏信号重构的近场源定位[J]. 电子学报, 2014, 42(6): 1041-1046.
[5]	陈诚, 陶建武, 曾宾. 数据缺失情况下的空气流动速度估计算法[J]. 电子学报, 2014, 42(3): 491-497.
[6]	李阳, 张建秋. 电磁矢量传感器阵列信号波达角估计的模R投影MUSIC[J]. 电子学报, 2014, 42(1): 107-112.
[7]	刘妍妍, 孙晓颖, 刘国红. 基于平行因子分析的近场循环平稳信源方位角和距离联合估计[J]. 电子学报, 2013, 41(10): 1958-1963.
[8]	倪淑燕;程乃平;倪正中. 共轭虚拟阵列波束形成方法[J]. 电子学报, 2011, 39(9): 2120-2124.
[9]	时洁;杨德森;时胜国. 基于组合阵列的水下特征线谱近场定位方法[J]. 电子学报, 2011, 39(6): 1231-1237.
[10]	黄庆东;张林让;王纯;张波. 基于数据选择Householder多级维纳滤波导航接收机空时抗干扰方法[J]. 电子学报, 2011, 39(6): 1368-1372.
[11]	熊子源;徐振海;张亮;吴迪军;肖顺平. 基于聚类算法的最优子阵划分方法研究[J]. 电子学报, 2011, 39(11): 2615-2621.
[12]	马克江;李军;吴云韬;廖桂生. 无需参数配对的近场源距离、方位、频率联合估计算法[J]. 电子学报, 2010, 38(6): 1454-1458.
[13]	顾陈何劲朱晓华. 冲击噪声背景下基于最小均方归一化误差的波束形成算法[J]. 电子学报, 2010, 38(6): 1430-1433.
[14]	张聪;胡谋法;卢焕章. 基于虚拟阵列空间平滑的相干信号DOA估计[J]. 电子学报, 2010, 38(4): 929-0933.
[15]	孙晓颖;王波;姜宏. 乘性噪声背景下基于三阶循环矩的二维近场源定位方法[J]. 电子学报, 2009, 37(9): 2068-2071.