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

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

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...

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Bibliographic Details
Main Authors: Xuehai Wang, Feng Ding, Qingsheng Liu, Chuntao Jiang
Format: Article
Language:English
Published: MDPI AG 2018-11-01
Series:Algorithms
Subjects:
recursive identification
least squares
bias compensation
state-space model
state estimation
Online Access:https://www.mdpi.com/1999-4893/11/11/175

Internet

https://www.mdpi.com/1999-4893/11/11/175

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