Least-squares identification of a class of multivariable systems with correlated disturbances

A valuable new way of taking advantage of signal processing techniques to i mplement unbiased parameter estimation was reported in Feng and Zheng (IEE Proc.-Control Theory Appl. 138 (1991) 484-492). In this paper, some extensi ons to the recently developed biase-liminated least-squares method are made such that the method can be employed to perform unbiased identification of multi-input-single-output systems subject to colored noise. A set of digit al prefilters are suitably designed to pre-process the input data sampled f rom multi-input channels, which gives rise to a system of linear equality c onstraints with respect to system parameters. Then combined with a bias cor rection procedure, the colored-noise-induced bias in the least-squares para meter estimators can be removed efficiently. The performance of the develop ed method is both analyzed theoretically and illustrated by means of simula tion results. (C) 2000 The Franklin Institute. Published by Elsevier Scienc e Ltd. All rights reserved.