Metamorphic moving horizon estimation

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 λ ∈ [0, 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.

Minimum variance constrained estimator

“…In this article, we keep our framework simple as [11] and focus on minimum variance duality based estimator. The recent results [46], [47] can be interesting future research directions along with the framework presented in this article.…”

“…Several interesting extensions of the proposed approach may be possible including controlled systems [16], systems with intermittent observations [45], distributed architecture [18] and inclusion of pre-estimating observer [26], [30]. The idea of pre-estimating observer [26] is recently combined with stochastic MHE [11] in [46], and the problem of unknown prior [30] is recently revisited in [47]. In this article, we keep our framework simple as [11] and focus on minimum variance duality based estimator.…”