Behaviour analysis of adaptive ARMA predictors with nonstationary inputs

The behavior of autoregressive moving average (ARMA) predictors is analyzed for neglected dynamic cases with nonstationary signals. For this analysis, nonstationarity types must be considered (abrupt or smooth changes in the mean and variance). Some procedures can be used to improve the algorithm performances and limit the self-stabilization oscillations. The resulting algorithm is not much more complex than the NLMS algorithm and can be used for applications like ADPCM or data compression with highly nonstationary signals.<
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