Square Hankel SVD subspace tracking algorithms
Signal Processing
The fast householder Bi-SVD subspace tracking algorithm
Signal Processing
Square-root Householder subspace tracking
Numerische Mathematik
The fast recursive row-Householder subspace tracking algorithm
Signal Processing
Bi-iterative least-square method for subspace tracking
IEEE Transactions on Signal Processing - Part II
Bi-iteration SVD subspace tracking algorithms
IEEE Transactions on Signal Processing
Fast recursive subspace adaptive ESPRIT algorithms
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Sliding window adaptive SVD algorithms
IEEE Transactions on Signal Processing
Hi-index | 35.68 |
A fast algorithm for computing the sliding window Bi-SVD subspace tracker is introduced. This algorithm produces, in each time step, a dominant rank-r SVD subspace approximant of an L × N rectangular sliding window data matrix. The method is based on the QS (orthonormal-square) decomposition. It uses two row-Householder transformations for updating and one nonorthogonal Householder transformation for downdating in each time step. The resulting algorithm is long-term stable and shows excellent numerical and structural properties, as known from pure Householder-type algorithms. The dominant complexity is 4Lr + 3Nr multiplications per time update, which is also the lower bound in dominant complexity for an algorithm of this kind. A completely self-contained algorithm summary is provided and a Fortran subroutine of the algorithm is available for download from http://webuser.hs-furtwangen.de/~strobach/qshbisvd.for.