Parameter Estimation Under Model Uncertainties by Iterative Covariance Approximation

We propose a novel iterative algorithm for estimating a de-terministic but unknown parameter vector in the presence of Gaussian 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. The proposed iterative algorithm can be applied on structured as well as unstruc-tured models and it outperforms prior art algorithms for a broad range of applications.