Fractional model based Kaiman filters for angular rate estimation in vestibular systems

The work presents the development and application of Bayesian recursive estimators such as Kalman filters for fractional models. Both linear and nonlinear fractional models, describing the relationship between the applied angular rate and the measured neuron firing rate in a vestibular system are used as benchmark problems for testing the proposed filtering schemes. The performance of the estimators is assessed both with respect to the approximation parameters and the filtering methods.