Noncausal nonminimum phase ARMA modeling of non-Gaussian processes

Athina P. Petropulu

doi:10.1117/12.190828

28 October 1994 Noncausal nonminimum phase ARMA modeling of non-Gaussian processes

Athina P. Petropulu

Proceedings Volume 2296, Advanced Signal Processing: Algorithms, Architectures, and Implementations V; (1994) https://doi.org/10.1117/12.190828
Event: SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, 1994, San Diego, CA, United States

Abstract

A method is presented for the estimation of the parameters of a noncausal nonminimum phase ARMA model for non-Gaussian random processes. Using certain higher-order cepstra slices, the Fourier phases of two intermediate sequences, h_min(n) and h_max(n), can be computed, where h_min(n) is composed of the minimum phase parts of the AR and MA models, and h_max(n) of the corresponding maximum phase parts. Under the condition that the AR and MA models do not have common zeros, these two sequences can be estimated from their phases only, and lead to the reconstruction of the AR and MA parameters, within a scalar and a time shift. The AR and MA orders do not have to be estimated separately, but they are a byproduct of the parameter estimation procedure. Unlike existing methods, the estimation procedure is fairly robust if a small order mismatch occurs. Since the robustness of the method in the presence of additive noise depends on the accuracy of the estimated phases of h_min(n) and h_max(n), the phase errors occurring due to finite length data are studied.

Citation Download Citation

Athina P. Petropulu "Noncausal nonminimum phase ARMA modeling of non-Gaussian processes", Proc. SPIE 2296, Advanced Signal Processing: Algorithms, Architectures, and Implementations V, (28 October 1994); https://doi.org/10.1117/12.190828

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