Tracking micro reentering USV with multi-sensors using fusion CKF algorithm

In this paper, a fusion CKF algorithm with TDRS (Tracking and Data Relay Satellite) and ground stations is presented for a micro reentering USV (Unmanned Space Vehicle). A micro reentering USV has high lift-drag ratio and maneuverability, unlike traditional reentry vehicle, is hard to track. In order to solve this problem, a fusion strategy of CKF (Cubature Kalman Filter) with multi-sensors is estimated. In the kinematics model, the aerodynamics parameters of reentering vehicle are designed. In the measurement models, although TDRS has more coverage capability than ground stations, a little angle error could cause a big error for the measurement data. Both TDRS and ground stations are presented. Meanwhile, in the filter algorithm, a new kind of filter named CKF is presented, which using spherical-radial cubature rule to numerically compute multivariate moment integrals encountered in the nonlinear filter. Also the fusion roles are used helping calculate the probabilities of multi-sensors. Simulation results show that the fusion CKF algorithm performs better than UKF in tracking sub-orbit USV and estimating the aerodynamics parameters.