An adaptive state estimation for tracking hypersonic glide targets with model uncertainties

Abstract

To track a maneuvering hypersonic glide target, an adaptive method for state estimation is proposed with model uncertainties in this paper. Various maneuver modes lead to considerable motion model uncertainty. In the method, unknown aerodynamic accelerations are modeled as the Singer model with a small maneuver frequency. When the real motion mode deviates from the default model, the maneuver frequency is adaptively enlarged by a novel technique derived from the orthogonal principle to reduce the model error. Simultaneously, the Huber technique is adopted to address measurement model uncertainty. Two strategies are specially formulated to alleviate possible misjudgments that a type of model error activates the non-corresponding technique. The adaptive state estimation is realized under the framework of unscented Kalman filter. Through tracking different flight trajectories, simulation results demonstrate that the proposed method has stronger robustness and higher estimation accuracy than conventional methods in the presence of model uncertainties and the computation burden is significantly less than the multiple-model method.