吉林大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (5): 1724-1730.doi: 10.13229/j.cnki.jdxbgxb201505049

• • 上一篇    下一篇

非均匀噪声下基于BOOTSTRAP和特征空间投影的信源数估计

郭立民, 冯凯   

  1. 哈尔滨工程大学 信息与通信工程学院,哈尔滨 150001
  • 收稿日期:2013-12-16 出版日期:2015-09-01 发布日期:2015-09-01
  • 作者简介:郭立民(1977-）,男,副教授.研究方向:宽带信号处理、检测与识别.E-mail:guolimin@hrbeu.edu.cn
  • 基金资助:
    国家自然科学基金项目(61202410)

Source detection based on characteristic subspace projection and Bootstrap technique in nonuniform noise

GUO Li-min, FENG Kai   

  1. College of Information and Telecommunication, Harbin Engineering University, Harbin, 150001, China
  • Received:2013-12-16 Online:2015-09-01 Published:2015-09-01

RICH HTML

0   

PDF (PC)

202

摘要： 针对阵列系统接收数据有限且存在空间非均匀噪声的信源数估计问题,提出一种基于Bootstrap和特征子空间投影的新方法。该方法将阵列信号的协方差矩阵分别投影到信号的特征子空间和噪声的特征子空间,提出了以特征空间投影为基础的一系列假设检验。数据分布未知,应用Bootstrap技术估计零假设下检测统计量的分布。通过仿真证明了所提方法在信源等功率、不等功率以及噪声功率不等的情况下有较好的性能,尤其在低信噪比和小快拍数的情况下有较好的性能。

关键词: 信息处理技术, 信源数检测, Bootstrap, 特征空间投影, 假设检验

Abstract: Considering the problem of source number estimation in the presence of unknown spatially nonumiform noise and the limitation of the received data, a now method based on Bootstrap technique and characteristic subspace projection is proposed. The estimation projects covariance matrix estimate of array signal into signal eigen subspace and noise eigen subspace respectively. A sequential hypothesis test is formulated based on characteristic subspace projection. No assumption is made in the distribution of the data and the Bootstrap is used to estimate the distribution under the null of the proposed test statistics. Simulation results show that the proposed method has good performances when the signal power is equal or different and under the condition of space-nonuniform noise environment, especially in the low signal-to-noise ratio and small snapshots.

Key words: information processing technology, source detection, bootstrap technique, characteristic subspace projection, hypothesis

中图分类号: 

  • TN911.23
[1] Zoubir A M.Bootstrap methods for model selection[J]. International Journal of Electronics and Communications, 1999, 53(6):386-392.
[2] Zoubir A M, Iskander D R. Bootstrap Techniques for Signal Processing[M]. Cambridge: Cambridge University Press, 2004.
[3] Lawley D. Tests of significance for the latent roots of covariance and correlation matrices[J]. Biometrika, 1956, 43:128-136.
[4] Chen W, Wong K M, Reilly J P. Detection of the number of signals: a predicted eigen-threshold approach[J]. IEEE Trans on Signal Processing, 1991, 39(5):1088-1098.
[5] Wax M, Kailath T. Detection of signals by information theoretic criteria[J]. IEEE Transactions Acoustics Speech Signal Processing, 1985, 33(2):387-392.
[6] Fishler E, Messer H. Order statistics approach for determining the number of sources using an array of sensors[J]. IEEE Signal Processing Letter, 1999, 6:179-182.
[7] Huang L, So H C. Source enumeration via MDL criterion based on linear shrinkage estimation of noise subspace covariance matrix[J]. IEEE Transactions on Signal Processing, 2013, 61(19):4806-4821.
[8] Lu Z H, Zoubir A M. Source enumeration using the PDF of sample eigenvalues via information theoretic criteria[C]∥IEEE International Conference on Acoustics Speech Signal Processing, Kyoto, Japan, 2012, 2:3361-3364.
[9] Pesavento M, Gershman A B. Maximum-likelihood direction-of-arrival estimation in the presence of unknown nonuniform noise[J]. IEEE Transactions on Signal Processing, 2001, 49(7):1310-1324.
[10] Wu H T, Yung J F, Chen F K. Source number estimator using gerschgorin disks[J]. International Conference on Acoustics Speech Signal Processing, Adelaide Australia, 1994, 4(4):261-264.
[11] Wu H T, Yung J F, Chen F K. Source number estimator using transformed gerschgorin radii[J]. IEEE Transactions On Signal Processing, 1995, 43(6):1325-1333.
[12] Yung J F, Wu H T. Gerschgorin radii based source number detection for closely spaced signals [C]∥IEEE Transactions Acoustics Speech Signal Processing, Atlanta May,1996:3054-3057.
[13] Gu J F, Wei P, Tai H M. Detection of the number of sources at low signal-to-noise ratio[C]∥IET Signal Processing,2007, 1(1):2-8.
[14] Zoubir A, Boashash B. The bootstrap and its application in signal processing[C]∥IEEE Signal Processing Magazine, 1998, 1:55-76.
[15] Politis D. Computer-intensive methods in statistical analysis[J]. IEEE Signal Processing Magazine,1998, 1:39-55.
[16] Aouada S, Zoubir A M, See C M S. Source detection in the presence of nonuniform noise[C]∥IEEE International Conference on Acoustics Speech Signal Processing, Montreal, Canada, 2004, 2:165-168.
[17] Scharf L L. Statistical Signal Processing: Detection, Estimation and Time Series Analysis[M]. Addison Wesley, 1990.
[18] Zoubir A, Iskander D. Bootstrap Techniques for Signal Processing[M]. Cambridge: Cambridge University Press, 2004.
[19] Aouada S, Transkov D, Heureuse N, et al. Application of the bootstrap to source detection in Nonuniform noise[C]∥IEEE International Conference on Acoustics Speech Signal Processing, Pennsylvania, USA, 2005, 4:997-1000.
[20] Brcich R, Zoubir A M, Pelin P. Detection of sources using bootstrap techqiques[J]. IEEE Transactions on Signal Processing, 2002, 50(2):206-215.
[21] Eforn B, Tibshirani R. An Introduction to the Bootstrap[M]. London:Chapman&Hall, 1993.
[1] 苏寒松,代志涛,刘高华,张倩芳. 结合吸收Markov链和流行排序的显著性区域检测[J]. 吉林大学学报(工学版), 2018, 48(6): 1887-1894.
[2] 徐岩,孙美双. 基于卷积神经网络的水下图像增强方法[J]. 吉林大学学报(工学版), 2018, 48(6): 1895-1903.
[3] 黄勇,杨德运,乔赛,慕振国. 高分辨合成孔径雷达图像的耦合传统恒虚警目标检测[J]. 吉林大学学报(工学版), 2018, 48(6): 1904-1909.
[4] 李居朋,张祖成,李墨羽,缪德芳. 基于Kalman滤波的电容屏触控轨迹平滑算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1910-1916.
[5] 应欢,刘松华,唐博文,韩丽芳,周亮. 基于自适应释放策略的低开销确定性重放方法[J]. 吉林大学学报(工学版), 2018, 48(6): 1917-1924.
[6] 陆智俊,钟超,吴敬玉. 星载合成孔径雷达图像小特征的准确分割方法[J]. 吉林大学学报(工学版), 2018, 48(6): 1925-1930.
[7] 刘仲民,王阳,李战明,胡文瑾. 基于简单线性迭代聚类和快速最近邻区域合并的图像分割算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1931-1937.
[8] 单泽彪,刘小松,史红伟,王春阳,石要武. 动态压缩感知波达方向跟踪算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1938-1944.
[9] 姚海洋, 王海燕, 张之琛, 申晓红. 双Duffing振子逆向联合信号检测模型[J]. 吉林大学学报(工学版), 2018, 48(4): 1282-1290.
[10] 全薇, 郝晓明, 孙雅东, 柏葆华, 王禹亭. 基于实际眼结构的个性化投影式头盔物镜研制[J]. 吉林大学学报(工学版), 2018, 48(4): 1291-1297.
[11] 陈绵书, 苏越, 桑爱军, 李培鹏. 基于空间矢量模型的图像分类方法[J]. 吉林大学学报(工学版), 2018, 48(3): 943-951.
[12] 陈涛, 崔岳寒, 郭立民. 适用于单快拍的多重信号分类改进算法[J]. 吉林大学学报(工学版), 2018, 48(3): 952-956.
[13] 孟广伟, 李荣佳, 王欣, 周立明, 顾帅. 压电双材料界面裂纹的强度因子分析[J]. 吉林大学学报(工学版), 2018, 48(2): 500-506.
[14] 林金花, 王延杰, 孙宏海. 改进的自适应特征细分方法及其对Catmull-Clark曲面的实时绘制[J]. 吉林大学学报(工学版), 2018, 48(2): 625-632.
[15] 王柯, 刘富, 康冰, 霍彤彤, 周求湛. 基于沙蝎定位猎物的仿生震源定位方法[J]. 吉林大学学报(工学版), 2018, 48(2): 633-639.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 《吉林大学学报(工学版)》编辑部
地址：长春市人民大街5988号　电话：+86-431-85095297　传真：+86-431-85094074　E-mail: xbgxb@jlu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发