Parallel and stable spherical subspace tracking

We introduce a factored spherical SVD updating algorithm which can be used for subspace tracking. It is a non-iterative algorithm for approximate SVD updating. The orthogonal matrix tracking the signal subspace is parameterized as a sequence of Givens rotations. This factorization has two important advantages. On the algorithmic level it cures the error accumulation problem inherent in the algorithm. The subspace matrix is now confined to the manifold of orthogonal matrices at all time. On the architectural level the factored algorithm is more amenable to parallel even systolic-implementation. Moreover, the SFG contains only rotation nodes. Therefore, an ideal processor for a real-time parallel ASIC architecture is a CORDIC processor