Optimal weighted LS AR estimation in presence of impulsive noise

A procedure for assigning optimal weights to the prediction equations which are used to obtain the parameters of an autoregressive (AR) model for spectrum estimation by the least squares (LS) solution is presented. The set of weights is computed, by linear programming techniques, in order to reduce the effects of strong impulsive noise onto the AR parameter estimate. The method is particularly effective when the Gaussian white noise component is much smaller than both spikes and useful signal. In order to demonstrate the capability of the proposed approach, the results of a simple AR parameter estimation experiment are also reporte