Hypersonic Gliding Reentry Vehicle Tracking with Process Noise Variance Adaptive Approach

Maneuvering reentry vehicle (MaRV) model based multiple model algorithm has been tried to track the hypersonic gliding reentry vehicle (HGRV), while there exists a process noise covariance difficulty to modulate. The aerodynamic parameters are assumed to be the first-order Markov processes. The aerodynamic parameter variances of the stochastic reentry ballistic trajectory are prior analyzed, based on which the process noise variances are obtained, and the uniform model set is designed. The target state is estimated with the autonomous multiple-model (AMM) approach, and the expected model augmentation (EMA) is used for a better performance. The HGRV with a constant attack angle and with a type of controlled attack angle are considered, respectively. Simulation results show that the proposed algorithm has a better performance than present algorithms.