Parameter Estimation Under Model Uncertainties by Iterative Covariance Approximation

TL;DR

This paper introduces an iterative algorithm for estimating unknown parameters in models with uncertainties, effectively handling combined measurement and model noise, and outperforming existing methods across various applications.

Contribution

The paper presents a novel iterative covariance approximation algorithm that accounts for model uncertainties in parameter estimation, applicable to both structured and unstructured models.

Findings

Outperforms prior algorithms in diverse applications

Handles combined measurement and model noise effectively

Applicable to structured and unstructured models

Abstract

We propose a novel iterative algorithm for estimating a deterministic but unknown parameter vector in the presence of model uncertainties. This iterative algorithm is based on a system model where an overall noise term describes both, the measurement noise and the noise resulting from the model uncertainties. This overall noise term is a function of the true parameter vector, allowing for an iterative algorithm. The proposed algorithm can be applied on structured as well as unstructured models and it outperforms prior art algorithms for a broad range of applications.