2002Unpublished venueRequires access

Statistics of the phase delays between array receivers estimated from the eigendecomposition of the signal correlation matrix

J.J. Smith, Y.H. Leung, A. Cantoni

Open publisher page 3 citations

Abstract

The eigenvector method for estimating the positions of the receivers of an ocean-towed array is based on the eigendecomposition of the array signal correlation matrix to find the phase delays between the array receivers. This paper derives the mean, variance and covariance of the phase delays when the correlation matrix is estimated from receiver data. It is shown that the variance of the relative phase delays is inversely proportional to the number of snapshots and SNR but relatively independent of the size of the correlation matrix. The theoretical derivations are supported by numerical simulations.>

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What this paper is about

The eigenvector method for estimating the positions of the receivers of an ocean-towed array is based on the eigendecomposition of the array signal correlation matrix to find the phase delays between the array receivers. This paper derives the mean, variance and covariance of the phase delays when the correlation matrix is estimated from receiver data. It is shown that the variance of the relative phase delays is inversely proportional to the number of snapshots and SNR but relatively independent of the size of the correlation matrix. The theoretical derivations are supported by numerical simulations.>

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Available abstract

The eigenvector method for estimating the positions of the receivers of an ocean-towed array is based on the eigendecomposition of the array signal correlation matrix to find the phase delays between the array receivers. This paper derives the mean, variance and covariance of the phase delays when the correlation matrix is estimated from receiver data. It is shown that the variance of the relative phase delays is inversely proportional to the number of snapshots and SNR but relatively independent of the size of the correlation matrix. The theoretical derivations are supported by numerical simulations.>

Key concepts: Eigendecomposition of a matrix, Covariance matrix, Eigenvalues and eigenvectors, Statistics, SIGNAL (programming language), Correlation, Matrix (chemical analysis), Mathematics

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