一种基于l_1范数的目标源测向算法
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- 英文论文题名：A Target Source Direction Estimation Algorithm Based On l_1 Norm
- 论文作者：李枝灵 ; 亓峰 ; 郭少勇 ; 邱雪松
- 英文论文作者：Zhiling Li ; Feng Qi ; Shaoyong Guo ; Xuesong Qiu ; State Key Laboratory of Networking and Switching Technology ; Beijing University of Posts and Telecommunications
- 年：2016
- 作者机构：北京邮电大学网络与交换技术国家重点实验室;
- 论文关键词：高维信号处理 ; e_1范数 ; 二阶锥规划算法 ; 稀疏重构 ; 信号角度估计
- 英文论文关键词：high-dimensional signal processing ; e_1norm ; SOC ; sparse reconstruction ; signal angle estimation
- 会议召开时间：2016-06-24
- 会议录名称：2016年全国通信软件学术会议程序册与交流文集
- 语种：中文
- 分类号：TN911.7
- 学会代码：ZGTH
- 会议名称：2016年全国通信软件学术会议
- 会议地点：中国陕西西安
- 主办单位：中国通信学会
- 学会名称：中国通信学会
- 页数：6
- 文件大小：966k
- 原文格式：O
- 会议级别：全国
摘要
在高维信号处理中,为了有效地估计信号的角度,提出了基于e_1范数的二阶锥规划算法(e_1-SVD)。该算法将稀疏重构用于目标源测向技术,在窄带信号的模型基础上,引进稀疏域模型,将一个高维信号的角度估计问题抽象成欠定方程组求解问题。经MATLAB仿真验证,与其他最小范数法以及经典MUSIC算法相比,该算法在较大的信噪比范围内都能取得较低的重构误差和较高的成功概率,对相关性较大的信号也能进行识别。这证明了该算法能够有效地实现目标源测向。
To estimate the angle signal in the high-dimensional signal processing efficiently,a second-order cone algorithm based on e_1 norm(e_1-SVD) is proposed.The algorithm applys sparse reconstniction to the target direction finding technology.Sparse domain model is introduced based on narrowband signal model,in which angle estimation problem of a high-dimensional signal is abstracted into underdetermined equations problems.Simulation results with MATLAB show that,compared with other minimum norm methods and classic MUSIC algorithm,e_1-SVD can achieve lower reconstruction error and a higher probability of success in a wide range of signal to noise ratio and identify the signal with larger correlation.So it is proved that the algorithm can achieve the target source direction estimation effectively.
To estimate the angle signal in the high-dimensional signal processing efficiently,a second-order cone algorithm based on e_1 norm(e_1-SVD) is proposed.The algorithm applys sparse reconstniction to the target direction finding technology.Sparse domain model is introduced based on narrowband signal model,in which angle estimation problem of a high-dimensional signal is abstracted into underdetermined equations problems.Simulation results with MATLAB show that,compared with other minimum norm methods and classic MUSIC algorithm,e_1-SVD can achieve lower reconstruction error and a higher probability of success in a wide range of signal to noise ratio and identify the signal with larger correlation.So it is proved that the algorithm can achieve the target source direction estimation effectively.
引文
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[3]Weber R.J.,Yikun Huang.Analysis for Capon and MUSIC DOA estimation algorithms[C].IEEE Antennas Propag Soc AP S Int Symp,North Charleston,2009.
[4]Michael Elad.Sparse and Redundant Representations:From Theory to Applications in Signal and Image Processing[M].New York:Springer-Verlag New York Inc,2010:3-76.
[5]胡南.稀疏重构的阵列信号波达方向估计算法研究[D].[博士论文].中国科学技术大学,2013.Hu Nan.Direction of Arrival Estimation Algorithms for Array Signals based on Sparse Signal Reconstruction[D].[Doctoral Thesis].University of Science and Technology of China,2013.
[6]周巍.L1范数最小化算法及应用[D].[硕士论文].华南理工大学,2013.Zhou Wei.LI-Norm Minimization Algorithms and Applications[D].[Master's Thesis].South China University of Technology,2013.
[7]李霖.基于稀疏感知理论的空间目标DOA估计算法研究[D].[硕士论文].北京理工大学,2015.Li Lin.Research on Direction-of-Arrival Estimation Algorithm of Space Targets Based on Sparse Sensing[D].[Master's Thesis].Beijing Institute of Technology,2015.
[8]吴光文,张爱军,王昌明.一种用于压缩感知理论的投影矩阵优化算法[J],电子与信息学报,2015,37(7):1681-1687.Wu Guangwen,Zhang Aijun,Wang Changming.Novel Optimization Method for Projection Matrix in Compress Sensing Theory[J],Journal of Electronics&Information Technology,2015,37(7):1681-1687.
[9]M.Fornasier,H.Rauhut.Iterative thresholding algorithms[J],Applied and Computational Harmonic Analysis,2008,25(2):187-208.
[10]Tian Yumin,Wang Zhihui.An adaptive orthogonal matching pursuit algorithm based on redundancy dictionary[C],Proceedings-201310th International Conference on Fuzzy Systems and Knowledge Discovery,Shenyang,China,2013:578-582.
[11]徐鹏,尧德中,陈华富.一种基于l_p模约束的FOCI_JSS迭代.EEG源定位新方法[J],电子学报,2006,34(1):55-58.Xu Peng,Rao Dezhong,Chen Huafu.A Novel EEG Source LocalizationM ethod Based on FOCUSS Iteration Procedure Combined with l_p Norm Sparse Constraints[J],Chinese Journal of Electronics,2006,34(1):55-58.
[12]S.Chen,D.Donoho,M.Saunders.Atomic Decomposition by Basis Pursuit[J],SIAM Review,2001,43(l):129-159.
[13]Gluhovsky,Ilya.Multinomial least angle regression[J].IEEE Transactions on Neural Networks and Learning Systems,2012,23(1):169-174.
[14]Tang.J,He.G,Fang.L.A new non-interior continuation method for second-order cone programming[J].Journal of Numerical Mathematics,2013,21(4):301-323.
[15]陈慧,邵怀宗,潘晔.基于稀疏表示的频控阵MIMO雷达多目标定位[J].雷达科学与技术,2015,13(3):259-264.Chen Hui,Shao Huanzong,Pan Ye.Multiple targets localization in sparse representation-based frequency diverse array MIMO radar[J].Radar Science and Technology,2015,13(3):259-264.
[16]韩树楠,李东生,雍爱霞,等.基于加权11-SRACV算法的稀疏DOA估计[J].现代雷达,2015,37(7):22-25.Han Shunnan,Li Dongsheng,Yong aixia,et al...Sparse DOA Estimation Based on Weighted ll-SRACV Algorithm[J].Modern Radar,2015,37(7):22-25.
[17]李一兵,黄辉,叶方,等.基于奇异值分解的压缩感知定位算法[J].中南大学学报(自然科学版),2014,45(5):1516-1521.LI Yibing,HUANG Hui,YE Fang,et al..Target localization via compressed sensing based on SVD[J].Journal of Central South University(Science and Technology),2014,45(5):1516-1521.