Robust recursive time series modeling based on an AR model excited by a t-distribution process

Abstract

In this correspondence, a new robust recursive spectral estimation based on an AR model is proposed. The optimal coefficients are selected by assuming that the excitation signal has a t-distribution t(/spl alpha/) with /spl alpha/ degrees of freedom. With /spl alpha/=/spl infin/, we get the RLS method. Simulation results show that the obtained estimates using the proposed method with small /spl alpha/ are more efficient, and the standard deviation (SD) of the estimation results is smaller and more accurate than that with large /spl alpha/. The proposed estimator with small /spl alpha/ is also more efficient and more accurate than the recursive method based on Huber's M estimate. Two approaches are used, i.e., the infinite memory and the exponentially weighted approaches.