BLIND IDENTIFICATION OF NONCAUSAL AR MODELS BASED ON HIGHER-ORDER STATISTICS

In this paper we develop a new method to identify noncausal AR models that are driven by non-Gaussian i.i.d. input. Under a few moderate ass umptions, the necessary and sufficient conditions for rebuilding the A R models from second and third order statistics are derived. It is sho wn that the AR model parameters are directly related to the solution o f an eigenproblem. Based on this approach we present a method of AR mo del identification, applying eigendecomposition. Unique identification of AR models is guaranteed up to sign and linear phase ambiguities. M odel order determination is not crucial in the method. If the order is overestimated, several equivalent AR models with different linear pha ses will be obtained.