Parameter identification in lumped linear continuous systems in a noisy environment via Kalman-filtered Poisson moment functionals

Abstract This paper presents a Poisson moment functional (PMF) approach to parameter identification in lumped linear continuous systems in a noisy environment. The method is based on initially Kalman-filtering the PMFs and then employing them in the established general algorithms. This Kalman-filtered Poisson moment Functional (KFPMF) method is shown to be superior to the conventional least squares approach through an illustrative example.