Two-Stage Parametric Identification Procedure for a Satellite Motion Model Based on Adaptive Unscented Kalman Filters

The paper presents a new two-stage parametric identification procedure for constructing a navigation satellite motion model. At the first stage of the procedure, the parameters of the radiation pressure model are estimated using the maximum likelihood method and the multiple adaptive unscented Kalman filter. At the second stage, the parameters of the unaccounted perturbations model are estimated based on the results of residual differences measurements. The obtained results lead to significant improvement of prediction quality of the satellite trajectory.

Keywords

nonlinear stochastic continuous-discrete system; multiple adaptive unscented Kalman filter; parametric identification; ML method; satellite orbital motion model.

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