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2010
IEEE

Utilizing principal singular vectors for two-dimensional single frequency estimation

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Utilizing principal singular vectors for two-dimensional single frequency estimation
In this paper, frequency estimation of a twodimensional (2D) cisoid in the presence of additive white Gaussian noise is addressed. By utilizing the rank-one property of the 2D noise-free data matrix, the frequencies are estimated in a separable manner from the principal left and right singular vectors according to an iterative weighted least squares procedure. We have also derived the mean and variance expressions for the frequency estimates, which show that they are approximately unbiased and their accuracy achieves Cram´erRao lower bound (CRLB) at sufficiently high signalto-noise ratio conditions. Computer simulation results are included to corroborate the theoretical development as well as to contrast the performance of the proposed algorithm with the weighted phase averager and iterative quadratic maximum likelihood method as well as CRLB.
H. C. So, Frankie K. W. Chan, C. F. Chan, W. H. La
Added 06 Dec 2010
Updated 06 Dec 2010
Type Conference
Year 2010
Where ICASSP
Authors H. C. So, Frankie K. W. Chan, C. F. Chan, W. H. Lau
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