Perspective Chapter: Insights from Kalman Filtering with Correlated Noises Recursive Least-Square Algorithm for State and Parameter Estimation

Abstract

This article explores the estimation of parameters and states for linear stochastic systems with deterministic control inputs. It introduces a novel Kalman filtering approach called Kalman Filtering with Correlated Noises Recursive Generalized Extended Least Squares (KF-CN-RGELS) algorithm, which leverages the cross-correlation between process noise and measurement noise in Kalman filtering cycles to jointly estimate both parameters and system states. The study also investigates the theoretical implications of the correlation coefficient on estimation accuracy through performance analysis involving various correlation coefficients between process and measurement noises. The research establishes a clear relationship: the accuracy of identified parameters and states is directly proportional to positive correlation coefficients. To validate the efficacy of this algorithm, a comprehensive comparison is conducted among different algorithms, including the standard Kalman filter algorithm and the augmented-state Kalman filter with correlated noises algorithm. Theoretical findings are not only presented but also exemplified through a numerical case study to provide valuable insights into practical implications. This work contributes to enhancing estimation accuracy in linear stochastic systems with deterministic control inputs, offering valuable insights for control system design and state-space modeling.