Selfperturbing recursive least squares algorithm with fast tracking capability

A novel recursive least squares (RLS) type algorithm with a selfperturbing action is devised. The algorithm possesses a fast tracking capability in itself because its adaptation gain is automatically revitalised through perturbation of the covariance update dynamics by the filter output error square when it encounters sudden parameter changes. Furthermore, the algorithm converges to the true parameter values in stationary environments.