Abstract
A method for simultaneous autoregressive (AR) model order selection and identification is proposed, which is based on the adaptive Lainiotis filter (ALF). The method is not restricted to the Gaussian case, is applicable to online/adaptive operation, and is computationally efficient. It can be realized in a parallel processing fashion. The AR model order selection and identification problem is reformulated so that it can be fitted into the framework of a state space under uncertainty estimation problem framework. The ALF is briefly presented and its application to the specific problem is discussed. Simulation examples are presented to demonstrate the superior performance of the method in comparison with previously reported ones.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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