The Bias Compensation Based Parameter and State Estimation for Observability Canonical State-Space Models with Colored Noise

Abstract

This paper develops a bias compensation-based parameter and state estimation algorithm for the observability canonical state-space system corrupted by colored noise. The state-space system is transformed into a linear regressive model by eliminating the state variables. Based on the determination of the noise variance and noise model, a bias correction term is added into the least squares estimate, and the system parameters and states are computed interactively. The proposed algorithm can generate the unbiased parameter estimate. Two illustrative examples are given to show the effectiveness of the proposed algorithm.

Keywords

• recursive identification
• least squares
• bias compensation
• state-space model
• state estimation