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

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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

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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

CLC Number: 

  • 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.
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