A new family of EVD tracking algorithms using Givens rotations

In this work, we derive new algorithms for tracking the eigenvalue decomposition (EVD) of a time-varying data covariance matrix. These algorithms have parallel structures, low operation counts and good convergence behavior. Their main feature is the use of Givens rotations to update the eigenvector estimates. As a result, orthonormality of the latter can be maintained at all time, which is critical in the application of certain signal-subspace methods. The comparative performance of the new algorithms is illustrated by means of computer experiments