A maximum a posteriori algorithm for reconstruction of targets in incompletely defined correlated noise

The maximum a posteriori (MAP) line spectral estimator used to characterize sinusoids in data corrupted by Gaussian noise of unknown correlation is generalized to the case where an experimental estimate of noise covariance is available. The estimator is robust to noise with mean square error and sta...

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Veröffentlicht in:IEEE transactions on signal processing 1998-05, Vol.46 (5), p.1439-1443
Hauptverfasser: Willis, A.J., De Mello Koch, R., Spear, B., Klopper, A.
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container_issue 5
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container_title IEEE transactions on signal processing
container_volume 46
creator Willis, A.J.
De Mello Koch, R.
Spear, B.
Klopper, A.
description The maximum a posteriori (MAP) line spectral estimator used to characterize sinusoids in data corrupted by Gaussian noise of unknown correlation is generalized to the case where an experimental estimate of noise covariance is available. The estimator is robust to noise with mean square error and standard deviation falling below that of the classical MAP for increasing number of samples, while approaching classical MAP for the case of no prior knowledge.
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subjects Acoustic noise
Africa
Applied sciences
Bayesian methods
Detection, estimation, filtering, equalization, prediction
Direction of arrival estimation
Exact sciences and technology
Frequency estimation
Gaussian noise
Information, signal and communications theory
Narrowband
Phased arrays
Sensor arrays
Signal and communications theory
Signal, noise
Telecommunications and information theory
Vectors
title A maximum a posteriori algorithm for reconstruction of targets in incompletely defined correlated noise
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