Adaptive Huber-Based Filter for Hypersonic Cruise Vehicle Navigation

Abstract

The navigation for hypersonic cruise vehicle (HCV) is a challenging task because of the complex vehicle dynamic and sensor measurement noise it suffered. This paper proposes a kind of adaptive robust Kalman filter using Mahalanobis distance for HCV navigation. The innovation-based adaptive estimation is discussed first. Based on Mahalanobis distance theory, a kind of robust covariance matrix estimation method is used to modify the innovation-based adaptive Kalman filter. Considering that the vehicle maneuver characteristics and noise statistics parameters varies during different periods, dual-frequency tuning for unknown noise statistics is designed based on this. The algorithm proposed by this paper is applied to hypersonic cruise vehicle navigation. Simulation has been made to verify the performance of the new algorithm according to HCV flight profile and characteristics; both Gaussian and non-Gaussian simulation are included.

A fast robust guidance scheme for a hypersonic vehicle in the diving phase

“…In this section, an optimal control allocation will be derived. The lift acceleration command L A:C corresponds with a required lift force coefficient C L:A:C calculated by equation (8). For achieving the commanded angle of attack, the equation shown as below should be solved…”

“…[4][5][6] Meanwhile, the navigation accuracy of the hypersonic vehicle will be customarily scathed and navigation errors with respect to the high-speed vehicles are evident. 7,8 Consequently, the guidance schemes, which consider the model uncertainties, navigation errors, and also can assure excellent properties remain the key technical challenges for the hypersonic vehicle.…”