A self-stabilized minor subspace rule

Abstract

In this letter, we present a minor subspace rule that extracts the subspace that spans the m minor components of a n-dimensional vector stationary random process, m<n. The algorithm is self-stabilizing such that the subspace vectors do not need to be periodically normalized to unit modulus, and the algorithm does not require matrix inversions or divides to… (More)

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