Adaptive SVD algorithm for covariance matrix eigenstructure computation

An adaptive algorithm is presented for covariance matrix eigenstructure computation based on the updated computation of the SVD (singular value decomposition) of a data matrix formed with the received data vectors appended as columns. Simulation results show that the algorithm is successful in tracking the eigenstructure of a time-varying covariance matrix in a nonstationary environment. The advantage of the algorithm is that it uses the data vectors X_i at each iteration to update the eigenstructure instead of a rank one matrix update, thus avoiding the need to double the dynamic range necessary for a given numerical accuracy. The computations for the algorithm are easily mapped on existing systolic arrays with some modifications