Estimation of aerodynamic parameter for maneuvering reentry vehicle tracking

Aim at handling complicated maneuvers or other unpredicted emergencies, an estimation method of aerodynamic parameter via the Cubature Kalman Filter/Smoother (CKF/S) is proposed for maneuvering reentry vehicle (MaRV) tracking. The aerodynamic model is deduced in order to define the aerodynamic parameter at first. Secondly, the statistical properties of aerodynamic parameters are described by first-order Markov processes, the random processes are carefully chosen and tuned for filtering. Meanwhile, the state model is achieved by augmented these parameters into state vector. Then, the CKF/S is introduced to deal with estimation of aerodynamic parameters. Particularly, a fixed interval (FI) CKS, which carries on forward filtering firstly and then backward smoothing in an interval of fixed time series, is applied to this problem. A comparative performance assessment of three estimation algorithms is presented. The results show that the FI-CKS performs better than the CKF. Moreover, it can achieve a better real-time character compared with the CKS.