Metamorphic Moving Horizon Estimation

TL;DR

This paper introduces a method to enhance existing estimators by gradually integrating moving horizon estimation (MHE) techniques through a tunable parameter, enabling a smooth upgrade of estimation performance.

Contribution

It proposes a general methodology to incorporate pre-existing estimators into MHE with a tuning parameter, allowing gradual performance improvement.

Findings

The method seamlessly integrates with classical estimators when λ=0.

Increasing λ enhances estimation performance gradually.

Applicable to standard MHE frameworks in literature.

Abstract

This paper considers a practical scenario where a classical estimation method might have already been implemented on a certain platform when one tries to apply more advanced techniques such as moving horizon estimation (MHE). We are interested to utilize MHE to upgrade, rather than completely discard, the existing estimation technique. This immediately raises the question how one can improve the estimation performance gradually based on the pre-estimator. To this end, we propose a general methodology which incorporates the pre-estimator with a tuning parameter {\lambda} between 0 and 1 into the quadratic cost functions that are usually adopted in MHE. We examine the above idea in two standard MHE frameworks that have been proposed in the existing literature. For both frameworks, when {\lambda} = 0, the proposed strategy exactly matches the existing classical estimator; when the value of {\lambda} is increased, the proposed strategy exhibits a more aggressive normalized forgetting effect towards the old data, thereby increasing the estimation performance gradually.