Abstract
To solve the problem that the ship’s strapdown inertial navigation system (SINS) alignment accuracy decreases with the increase of the nonlinear filtering state dimension under mooring conditions, a method based on Kalman filter (KF) and Adaptive scale mini-skewness single line sampling Unscented Kalman Filter (ASMUKF) hybrid filtering algorithm is proposed in this paper. Three improvements are made as the following: (1) adopt a new sampling strategy. To obtain the ASMUKF filtering algorithm, scale mini-skewness single line sampling is used to replaced the traditional symmetrical sampling method and an adaptive scale factor is adapted into the Unscented Kalman Filter (UKF) to correct the real-time transformation sampling process; (2) the improved ASMUKF algorithm is combined with KF to form KF-ASMUKF hybrid filtering model; (3) the hybrid filtering model is divided into linear and nonlinear parts. The linear filtering part adopts the KF filtering model and the nonlinear filtering part adopts the ASMUKF model. Then, the calculation steps of the hybrid filtering algorithm is designed in this paper. The simulation and experimental results show that the hybrid filtering algorithm proposed has certain advantages over the traditional algorithm, and it can reduce the ship’s SINS calculation amount and improve alignment accuracy under mooring conditions.
Highlights
The results show that in the case of large misalignment angles, Unscented Kalman Filter (UKF) has higher estimation accuracy than Extended
In [26], Rong et al proposed an adaptive filtering ship strapdown inertial navigation system (SINS) mooring alignment algorithm based on Complementary Ensemble Empirical Mode Decomposition (CEEMD), which eliminates the effect of sensor measurement noise on accuracy and improves alignment accuracy
We set up a swing experiment platform based on a fiber optic gyro strapdown inertial navigation system on the threeaxis turntable in the laboratory to simulate the marine environment of the ship
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The error propagation model and filtering algorithm are two important issues for studying the initial alignment of strapdown inertial navigation system [15]. In [20], Sun et al proposed an inertial system initial alignment algorithm based on hidden Markov model-Kalman filter (HMM-KF). In [26], Rong et al proposed an adaptive filtering ship SINS mooring alignment algorithm based on Complementary Ensemble Empirical Mode Decomposition (CEEMD), which eliminates the effect of sensor measurement noise on accuracy and improves alignment accuracy.
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.
