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Tracking the dominant subspace of a data matrix is an essential part of many signal processing algorithms. We present a modification to the so-called spherical subspace tracking algorithm. This algorithm has a low computational complexity, but suffers from accumulation of numerical errors. We show that this numerical instability can be circumvented by using a minimal orthogonal parameterization of the subspace basis matrix. The resulting factored spherical SVD updating algorithm then consists exclusively of rotation operations. In view of implementing the algorithm in real-time applications with high data rates, a linear systolic architecture is derived. |
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