An estimator of the number of sources based on a sequence of hypothesis test

Estimation of the number of sources embedded in noise is a fundamental problem in statistical signal and array processing. This paper focuses on a non-parametric tool to estimate the number of sources without any information about the signature matrix. We exploit the behavior of eigenvalues of the s...

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Bibliographic Details
Published in2011 International Conference on Computational Problem-Solving (ICCP) pp. 1 - 6
Main Authors Manlin Xiao, Janqi Lu, Ping Wei
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2011
Subjects
Arrays
Covariance matrix
Eigenvalues and eigenfunctions
Estimation
Estimator
Hypothesis Test
Non Parameterization
Number of sources
Random Matrix Theory
Signal processing algorithms
Signal to noise ratio
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Summary:Estimation of the number of sources embedded in noise is a fundamental problem in statistical signal and array processing. This paper focuses on a non-parametric tool to estimate the number of sources without any information about the signature matrix. We exploit the behavior of eigenvalues of the sample covariance matrix and propose a new estimator based on a sequence of hypothesis test. A series of simulations show its superiority compared to the classical estimators based on information theoretic criteria.
ISBN:9781457706028
1457706024
DOI:10.1109/ICCPS.2011.6092299