Implementation of unknown parameter estimation procedure for hybrid and discrete non-linear systems

Abstract

The application of the hybrid extended Kalman filter (HEKF), hybrid unscented Kalman filter (HUKF), hybrid particle filter (HPF), and hybrid extended Kalman particle filter (HEKPF) is discussed for hybrid non-linear filter problems, when prediction equations are continuous-time and the update equations are discrete-time, and also the discrete extended Kalman filter (DEKF), discrete unscented Kalman filter (DUKF), discrete particle filter (DPF), and discrete extended Kalman particle filter (DEKPF) for discrete-time non-linear filter problems, when prediction equations and update equations are discrete-time. In order to assess the performance of the filters, the authors consider the non-linear dynamics for a re-entry vehicle. The filters are used in two hybrid and discrete states to estimate the position, velocity, and drag parameter associated with the re-entry vehicle. Theoretical topics concerning estimating the drag parameter of a vehicle in re-entry phase have been dealt with. Drag parameter estimation is carried out using a combination of hybrid filters and discrete filters as an effective estimator and fixed value, forgetting factor, and Robbins-Monro stochastic approximation methods as the noise covariance matrix adjuster of the parameter.