Metamorphic Moving Horizon Estimation
For practitioners who have already deployed classical estimators and want to incrementally adopt MHE without discarding existing implementations.
The paper proposes a method to upgrade existing classical estimators to moving horizon estimation (MHE) by introducing a tuning parameter that gradually improves estimation performance, with the classical estimator recovered when the parameter is zero.
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 λ 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 λ = 0, the proposed strategy exactly matches the existing classical estimator; when the value of λ is increased, the proposed strategy exhibits a more aggressive normalized forgetting effect towards the old data, thereby increasing the estimation performance gradually.