An Alignment Method Based on KF-ASMUKF Hybrid Filtering for Ship’s SINS under Mooring Conditions

• Published in: Sensors (Basel, Switzerland) Switzerland), 2021-10, Vol.21 (21), p.7104
• Main Authors: Yao, Pengchao, Yang, Gongliu, Peng, Xiafu
• Format: Article
• Language: English
• Subjects: Accuracy; Adaptive sampling; Algorithms; ASMUKF; Decomposition; Design; hybrid filter algorithm; Inertial navigation; Kalman filters; Linear filters; Mooring; mooring alignment; Navigation systems; Sampling methods; SINS; Skewness; Strapdown inertial navigation; Velocity
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21217104

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.