Estimation of the parameters of multichannel autoregressive signals from noisy observations

This paper is concerned with estimation of multichannel autoregressive (MAR) model parameters using noisy observations. The NILS method proposed in W.X. Zheng [A new estimation algorithm for AR signals measured in noise, in: Proceedings of the ICSP Conference 1, 2002, pp. 186–189] for estimation of the parameters of noisy scalar autoregressive (AR) signals is generalized to the multichannel case. An improved least-squares-based parameter estimator is introduced so that the variance–covariance matrix of the multichannel noise can be estimated in an iterative manner. With this, the noise-induced estimation bias can be removed to yield the unbiased estimate of the MAR parameters. In a simulation study, the performance of the proposed unbiased estimation algorithm is evaluated and compared with that of the existing parameter estimation methods.