On unbiased identification of autoregressive signals with noisy measurements

The problem of identification of autoregressive (AR) signals with noisy measurements is considered. A new algorithm is proposed to estimate the AR parameters. To cope with the effect of the measurement noise that causes a bias in the least-squares estimate of the AR parameters, an efficient procedure is developed for estimating the measurement noise variance. The proposed identification algorithm is implemented via the Newton iterative scheme and is able to produce better parameter estimates. A numerical example is presented to show the efficiency of the new identification algorithm for noisy AR signals.