Showing papers by "Mats Viberg published in 1990"
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TL;DR: The Weighted Subspace Fitting (WSF) method is shown to be a unification of the subspace fitting techniques and the stochastic Maximum Likelihood method, valid for large amounts of data, in the area of sensor array processing.
98 citations
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03 Apr 1990TL;DR: Methods for estimating the parameters of narrowband signals arriving at an array of sensors are analyzed and results are shown to be valid under much more general conditions, i.e. the actual distribution of the signal waveforms does not affect the asymptotic properties of the parameter.
Abstract: Methods for estimating the parameters of narrowband signals arriving at an array of sensors are analyzed. Asymptotic results for several estimators have recently appeared in the literature. With few exceptions, the previous analysis requires the incident signal waveforms to be Gaussian random variables. These results are shown to be valid under much more general conditions, i.e. the actual distribution of the signal waveforms does not affect the asymptotic properties of the parameter. >
19 citations
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03 Apr 1990
TL;DR: The problem of estimating signal parameters from sensor array data is addressed, and a generalization of the ESPRIT algorithm is proposed by introducing row weighting of the subspace estimate.
Abstract: The problem of estimating signal parameters from sensor array data is addressed. If the array is composed of two identical subarrays, (i.e. one invariance) the ESPRIT algorithm is known to yield parameter estimates in a very cost efficient manner. Recently, the total least squares (TLS) version of ESPRIT has been formulated in a subspace fitting framework. In this formulation, the ESPRIT concept is easily generalized to arrays exhibiting more than one invariance. The asymptotic properties for this class of algorithms are derived. The estimates are shown to be statistically efficient under certain assumptions. The case of a uniform linear array is studied in more detail, and a generalization of the ESPRIT algorithm is proposed by introducing row weighting of the subspace estimate. >
8 citations