Abstract
A class of fast recursive subspace tracking algorithms based on the orthogonal iteration principle is introduced. Realizations with O( Nr 2) and O( Nr) complexity are derived, where N is the model order and r ⩽ N is the rank of the underlying data covariance matrix. Comparisons reveal that our algorithms require fewer operations and offer a better angle performance than the recently introduced TQR-SVD subspace tracker. Complete quasi-code tables of the algorithms are provided. Applications to adaptive frequency estimation and rank adaptive subspace filtering are described in detail.
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