An Efficient Method for the Joint Estimation of System Parameters and Noise Covariances for Linear Time-Variant Systems

TL;DR

This paper introduces an optimization-based approach for jointly estimating system parameters and noise covariances in linear time-variant systems, demonstrating improved efficiency and accuracy over traditional methods through numerical simulations.

Contribution

The paper proposes a novel structure-exploiting solver for joint estimation, enhancing computational efficiency and accuracy in moving horizon estimation frameworks.

Findings

Successfully estimates model parameters in short computational time

Outperforms traditional methods in accuracy and efficiency

Validated on a realistic thermal system example

Abstract

We present an optimization-based method for the joint estimation of system parameters and noise covariances of linear time-variant systems. Given measured data, this method maximizes the likelihood of the parameters. We solve the optimization problem of interest via a novel structure-exploiting solver. We present the advantages of the proposed approach over commonly used methods in the framework of Moving Horizon Estimation. Finally, we show the performance of the method through numerical simulations on a realistic example of a thermal system. In this example, the method can successfully estimate the model parameters in a short computational time.