Abstract
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.
Highlights
Unmanned underwater vehicle (UUV) is one of the most important platforms to explore and develop the ocean, which has great significance in the military
To demonstrate the accuracy and robustness of AH∞cubature Kalman filter (CKF) algorithm, the CKF, H∞ Cubature Kalman Filter (H∞CKF), and AH∞CKF algorithm are used to estimate the state of a nonlinear system. e algorithms will be verified by the following model, which obtains motion information through Doppler Velocity Log (DVL), compass, and inertial navigation system (INS)
We can see that the values of the position errors in the X-axis of the AH∞CKF algorithm are within 4 m, while those of CKF and H∞CKF are within 14 m and 7 m
Summary
Unmanned underwater vehicle (UUV) is one of the most important platforms to explore and develop the ocean, which has great significance in the military. E H∞ Cubature Kalman Filter (H∞CKF) is proposed to keep the advantages of both CKF and H∞ filter and promises good robustness in case of extreme noise. The influence of dynamic errors can be controlled by the Sage-Husa noise estimator, whose essence is to use the observation value to modify the prediction value continuously in the filtering process, and to modify the unknown or changing system parameters at the same time. With the increase of c, the filtering method would be more and more insensitive to the changes of system model error, initial state value, and noise statistical characteristics, and its robustness would decrease gradually. E H∞CKF minimizes the estimation error in the worst case, which makes it more robust than the cubature Kalman filter
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
