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

Previous Articles     Next Articles

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)

240

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.
[1] YING Huan,LIU Song-hua,TANG Bo-wen,HAN Li-fang,ZHOU Liang. Efficient deterministic replay technique based on adaptive release strategy [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1917-1924.
[2] LIU Zhong-min,WANG Yang,LI Zhan-ming,HU Wen-jin. Image segmentation algorithm based on SLIC and fast nearest neighbor region merging [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1931-1937.
[3] SHAN Ze-biao,LIU Xiao-song,SHI Hong-wei,WANG Chun-yang,SHI Yao-wu. DOA tracking algorithm using dynamic compressed sensing [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1938-1944.
[4] YAO Hai-yang, WANG Hai-yan, ZHANG Zhi-chen, SHEN Xiao-hong. Reverse-joint signal detection model with double Duffing oscillator [J]. 吉林大学学报(工学版), 2018, 48(4): 1282-1290.
[5] QUAN Wei, HAO Xiao-ming, SUN Ya-dong, BAI Bao-hua, WANG Yu-ting. Development of individual objective lens for head-mounted projective display based on optical system of actual human eye [J]. 吉林大学学报(工学版), 2018, 48(4): 1291-1297.
[6] CHEN Mian-shu, SU Yue, SANG Ai-jun, LI Pei-peng. Image classification methods based on space vector model [J]. 吉林大学学报(工学版), 2018, 48(3): 943-951.
[7] CHEN Tao, CUI Yue-han, GUO Li-min. Improved algorithm of multiple signal classification for single snapshot [J]. 吉林大学学报(工学版), 2018, 48(3): 952-956.
[8] MENG Guang-wei, LI Rong-jia, WANG Xin, ZHOU Li-ming, GU Shuai. Analysis of intensity factors of interface crack in piezoelectric bimaterials [J]. 吉林大学学报(工学版), 2018, 48(2): 500-506.
[9] LIN Jin-hua, WANG Yan-jie, SUN Hong-hai. Improved feature-adaptive subdivision for Catmull-Clark surface model [J]. 吉林大学学报(工学版), 2018, 48(2): 625-632.
[10] WANG Ke, LIU Fu, KANG Bing, HUO Tong-tong, ZHOU Qiu-zhan. Bionic hypocenter localization method inspired by sand scorpion in locating preys [J]. 吉林大学学报(工学版), 2018, 48(2): 633-639.
[11] YU Hua-nan, DU Yao, GUO Shu-xu. High-precision synchronous phasor measurement based on compressed sensing [J]. 吉林大学学报(工学版), 2018, 48(1): 312-318.
[12] WANG Fang-shi, WANG Jian, LI Bing, WANG Bo. Deep attribute learning based traffic sign detection [J]. 吉林大学学报(工学版), 2018, 48(1): 319-329.
[13] LIU Dong-liang, WANG Qiu-shuang. Instantaneous velocity extraction method on NGSLM data [J]. 吉林大学学报(工学版), 2018, 48(1): 330-335.
[14] TANG Kun, SHI Rong-hua. Detection of wireless sensor network failure area based on butterfly effect signal [J]. 吉林大学学报(工学版), 2017, 47(6): 1939-1948.
[15] LI Juan, MENG Ke-xin, LI Yue, LIU Hui-li. Seismic signal noise suppression based on similarity matched Wiener filtering [J]. 吉林大学学报(工学版), 2017, 47(6): 1964-1968.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd