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Maximum Likelihood Recursive Generalized Extended Least Squares Estimation Methods for a Bilinear-parameter Systems with ARMA Noise Based on the Over-parameterization Model

Haibo Liu*, Junwei Wang, and Yan Ji
International Journal of Control, Automation, and Systems, vol. 20, no. 8, pp.2606-2615, 2022

Abstract : Maximum likelihood methods have wide applications in system modeling and parameter estimation. For the purpose of improving the precision of parameter estimation, this paper presents a maximum likelihood recursive generalized extended least squares (ML-RLS) algorithm for a bilinear-parameter system with autoregressive moving average noise based on the over-parameterization identification model. An over-parameterization-based recursive generalized extended least squares algorithm is presented to show the effectiveness of the proposed ML-RLS algorithm for comparison. The simulation test shows that the proposed algorithm has a higher estimation accuracy than the recursive least squares algorithm.

Keyword : "Bilinear-parameter system, least squares, maximum likelihood, parameter estimation, recursive identification. "

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