On-line Subspace Estimation Using a Generalized Schur Method
نویسندگان
چکیده
A new method is presented for estimating the column space (signal subspace) of a low rank data matrix distorted by additive noise. It is based on a tangible expression for the set of all matrices of minimal rank that are ε-close to the data matrix in matrix 2-norm. The usual truncated SVD approximant is contained in this set. Features of the algorithm are (1) it has the same computational structure and complexity as a QR factorization of the data matrix, (2) it yields an on-line scheme, amenable to parallel (systolic) implementation, (3) updating and downdating is straightforward, (4) a rank decision (to detect the number of signals) is automatic, for a given threshold ε. It is shown in simulations on a typical direction finding application that the algorithm exhibits similar performance as SVD-based methods, at a fraction of the computational cost.
منابع مشابه
On-line subspace estimation using a Schur-type method
A recently developed Schur-type matrix approximation technique is applied to subspace estimation. The method is applicable if an upper bound of the noise level is approximately known. The main feature of the algorithm is that updating and downdating is straightforward and efficient, and that the subspace dimension is elegantly tracked as well.
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