AbstractGuidance, navigation, and control of a hypersonic vehicle landing on the Mars rely on precise state feedback information, which is obtained from state estimation. The high uncertainty and nonlinearity of the entry dynamics make the estimation a very challenging problem. In this paper, a new adaptive cubature Kalman filter is proposed for state trajectory estimation of a hypersonic entry vehicle. This new adaptive estimation strategy is based on the measure of nonlinearity of the stochastic system. According to the severity of nonlinearity along the trajectory, the high degree cubature rule or the conventional third degree cubature rule is adaptively used in the cubature Kalman filter. This strategy has the benefit of attaining higher estimation accuracy only when necessary without causing excessive computation load. The simulation results demonstrate that the proposed adaptive filter exhibits better performance than the conventional third-degree cubature Kalman filter while maintaining the same performance as the uniform high degree cubature Kalman filter but with lower computation complexity.
HighlightsAn adaptive cubature Kalman filter (ACKF) was designed.ACKF provides precise state estimation for hypersonic entry vehicles.ACKF adjusts the accuracy degree according to nonlinearity of the system.ACKF outperforms the conventional third-degree CKF.ACKF achieves the same performance as the fifth-degree CKF with less computation.
Hypersonic entry vehicle state estimation using nonlinearity-based adaptive cubature Kalman filters
Acta Astronautica ; 134 ; 221-230
2017-01-06
10 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Hypersonic entry vehicle state estimation using nonlinearity-based adaptive cubature Kalman filters
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