A Novel Adaptive H-Infinity Cubature Kalman Filter Algorithm Based on Sage-Husa Estimator for Unmanned Underwater Vehicle

In the navigation of unmanned underwater vehicle (UUV), a filtering algorithm suitable for the working conditions is required. Due to the disturbance from the environment and maneuverability, outliers and noise with time-varying statistical properties always exist, which greatly affect the positioning accuracy and stability of the navigation system. In this paper, we present a novel nonlinear state estimation algorithm named AH∞CKF based on the combination of H∞CKF and Sage-Husa estimator. The recently developed H∞CKF provides nonlinear filtering good robustness, and Sage-Husa estimator could timely modify the statistical properties of noise. The novel algorithm is superior to H∞CKF in accuracy by combining Sage-Husa estimator with the H∞CKF while ensuring robustness. The effectiveness of the novel AH∞CKF is verified by lake experiment and simulation.