Recurrent identification of autoregression in the presence of observation noises in output signal

The paper introduces a recurrent algorithm for parameterizing autoregression in the presence of observation noises in output signal. It has been proven that the values obtained are strongly consistent. To apply the developed algorithm there is no need to know either the noise or the signal propagation laws. The numerical experiments conducted have proven high efficiency of the suggested identification method.