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

A valuable new way of taking advantage of signal processing techniques to implement unbiased parameter estimation was reported in Feng and Zheng (IEE Proc.-Control Theory Appl. 138 (1991) 484-492). In this paper, some extensions 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 digital prefilters are suitably designed to pre-process the input data sampled from multi-input channels, which gives rise to a system of linear equality constraints with respect to system parameters. Then combined with a bias correction procedure, the colored-noise-induced bias in the least-squares parameter estimators can be removed efficiently. The performance of the developed method is both analyzed theoretically and illustrated by means of simulation results. (C) 2000 The Franklin Institute. Published by Elsevier Science Ltd. All rights reserved.