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Paper Detail

Paper:	SPTM-P8.4
Session:	Adaptive Systems and Filtering II
Time:	Thursday, May 18, 14:00 - 16:00
Presentation:	Poster
Topic:	Signal Processing Theory and Methods: Adaptive Systems and Filtering
Title:	Principal And Minor Subspace Tracking: Algorithms & Stability Analysis
Authors:	Steve Bartelmaos, Karim Abed-Meraim, ENST Paris, France
Abstract:	We consider the problem of tracking the minor or principal subspace of a positive Hermitian covariance matrix. We first propose a fast and numerically robust implementation of Oja algorithm (FOOja: Fast Orthogonal Oja). The latter is said fast in the sense that its computational cost is of order O(np) flops per iteration where n is the size of the observation vector and p < n is the number of minor or principal eigenvectors we need to estimate. FOOja guarantees the orthogonality of the weight matrix at each iteration. Moreover, this algorithm is analyzed and compared with two other fast algorithms (OOjaH and FDPM) with respect to their numerical stability. This analysis highlights the relatively good stability behavior of FOOja that is illustrated later by simulation results.