This paper focuses on nonminimum phase FIR system identification using higher-order cyclic cumulants alone, and proposes two linear normal equation algorithms for parameter estimation and SVD-based order determination methods. The system unidentifiable from second-order cyclostationary statistics are shown to be identifiable using the proposed higher-order cyclic cumulants based algorithms. Simulations are given to verify the high performance of the new methods.
In this paper, a new noncausal AR system identification method based on cepstrum is presented. The proposed method converts the noncausal AR system identification to nonminimum phase MA system identification in the cepstrum domain. The AR order need not be known as a priori, and it is a byproduct of the parameter estimation procedure. An efficient order determination method that can be implemented in an adaptive process is also addressed. Simulation results are presented to show the performance of the new method.
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