Solving the SVD updating problem for subspace tracking on a fixed sized linear array of processors

This paper addresses the problem of tracking the covariance matrix eigenstructure, based on SVD (singular value decomposition) updating, of a time-varying data matrix formed from the received vectors. This problem occurs frequently in signal processing applications such as adaptive beamforming, direction finding, spectral estimation, etc. As this problem needs to be solved in real time, it is natural to look for a parallel algorithm so that computation time can be reduced by distributing the work among a number of processing units. This paper proposes a parallel scheme for SVD updating that can be implemented on a fixed sized array of off-the-shelf processors, to get speedups close to the number of processors used