The singular value decomposition (SVD) is an important tool for subspace estimation. In adaptive signal processing, we are especially interested in tracking the SVD of a recursively updated data matrix. This paper introduces a new tracking technique that is designed for rectangular sliding window data matrices. This approach, which is derived from the classical bi-orthogonal iteration SVD algorithm, shows excellent performance in the context of frequency estimation. It proves to be very robust to abrupt signal changes, due to the use of a sliding window. Finally, an ultra-fast tracking algorithm with comparable performance is proposed.
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Badeau et al. (2004) studied this question.
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