A new efficient subspace tracking algorithm based on singular value decomposition

A new algorithm for signal subspace tracking is presented. It is based on an approximated singular value decomposition using interlaced QR-updating and Jacobi plane rotations. By forcing the noise subspace to be spherical, the computational complexity of the algorithm is brought down to O(nr), where n is the problem dimension and r is the desired number of signal components. The algorithm lends itself for a very efficient systolic array implementation, resulting in a throughput of O(n/sup 0/). Simulations show that the frequency tracking capabilities of the new method are at least as good as those of the computationally much more expensive exact singular value decomposition.