NDLI: Subspace estimation using factor analysis

Content Provider	IEEE Xplore Digital Library
Author	Sardarabadi, A.M. van der Veen, A.
Copyright Year	2012
Description	Author affiliation: Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology (TU Delft), Germany (Sardarabadi, A.M.; van der Veen, A.)
Abstract	Many subspace estimation techniques assume either that the system has a calibrated array or that the noise covariance matrix is known. If the noise covariance matrix is unknown, training or other calibration techniques are used to find it. In this paper another approach to the problem of unknown noise covariance is presented. The complex factor analysis (FA) and a new extended version of this model are used to model the covariance matrix. The steep algorithm for finding the MLE of the model parameters is presented. The Fisher information and an expression for the Cramér-Rao bound are derived. The practical use of the model is illustrated using simulated and experimental data.
Starting Page	477
Ending Page	480
File Size	250360
Page Count	4
File Format	PDF
ISBN	9781467310703
ISSN	2151870X
e-ISBN	9781467310710
DOI	10.1109/SAM.2012.6250543
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise Covariance matrix Arrays Maximum likelihood estimation Data models maximum-likelihood Factor analysis complex factor analysis subspace estimation Cramér-Rao bound
Content Type	Text
Resource Type	Article

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