Mathew, G and Reddy, VU and Dasgupta, S (1992) Gauss-Newton Based Adaptive Subspace Estimation. In: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP-92., 23-26 March, San Francisco,CA, Vol.4, 205-208.
In this paper, we present an adaptive approach for estimating all (or some) the orthogonal eigenvectors of the data covariance matrix (of a time series consisting of real narrowband in additive white noise). We use inflation approach to estimate each of these vectors as minimum eigenvectors (eigenvectors corresponding to the minimum eigenvalue) of appropriately constructed symmetric positive defitematrices. This reformulation of the problem is made possible by the fact that the problem of estimating the minimum eigenvector of a symmetric positive definite matrix can be restated as the unconstrained minimization of an appropriately constructed non-linear non-convex cost function. The modular nature of the algorithm. that results from this reformulation, makes the proposed approach highly parallel, resulting in a high-speed adaptive approach for subspace estimation.
|Item Type:||Conference Paper|
|Additional Information:||Copyright 1992 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE|
|Department/Centre:||Division of Electrical Sciences > Electrical Communication Engineering|
|Date Deposited:||30 May 2006|
|Last Modified:||19 Sep 2010 04:27|
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