Estimation of the parameters of multichannel autoregressive signals from noisy observations

Authors:
Alimorad Mahmoudi;Mahmood Karimi
Affiliations:
Electrical Engineering Department, Shiraz University, Zand Street, Namazi Square, Shiraz, Iran;Electrical Engineering Department, Shiraz University, Zand Street, Namazi Square, Shiraz, Iran
Venue:
Signal Processing
Year:
2008

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Abstract

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.