An extension of the PASTd algorithm to both rank and subspace tracking

In this letter, we present an extension of the PASTd algorithm to both rank and signal subspace tracking. It has a low computational complexity O(nr), where n is the input vector length, and r denotes the signal subspace dimension. Its performance in tracking time-varying direction of arrival is comparable with that of the expensive eigenvalue decomposition and more robust than the O(n/sup 2/) rank revealing URV updating algorithm proposed by Stewart.<
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