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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 timesN 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/qsh-bisvd.for.