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Adaptive Kalman Filtering for Trajectory Estimation of Hypersonic Glide Reentry Vehicles

WU Nan,CHEN Lei College of Aerospace Science and Engineering,National University of Defense Technology, Changsha 410073,China  
The trajectory estimation of a hypersonic glide reentry vehicle(HGRV) usually uses traditional state augment methods,which have very large model simplification errors and the process noise variance of which are hard to build.In this study,based on the quantitative analysis results of the target movement property and the model simplification errors,the state equations are refined by using the spherical gravity model and fitting atmosphere model and considering the Coriolis force.The aerodynamic parameters are described using the first-order Markov process,and then the process noise variance is formulated as a function of the aerodynamic parameter variance and maneuvering time constant.Moreover,the time-varying aerodynamic parameter variance is obtained using the statistical result of the aerodynamic parameter estimate sequence based on the "fading memory" method,while the maneuvering time constant,as a target movement mode,is estimated along with the target base state by using a multi-model method of expected model augmentation.The simulation results show that the proposed algorithm can identify the time-varying variance of process noise effectively,and demonstrates better performance than traditional algorithms in the estimation precision of position,velocity and aerodynamics parameters,and has better engineering application value,robustness,and effectiveness-cost ratio.
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