Yao Pengchao, Yang Gongliu, Peng Xiafu
Department of Automation, Xiamen University, Xiamen 361005, China.
School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China.
Sensors (Basel). 2021 Oct 26;21(21):7104. 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.
为解决船舶捷联惯性导航系统（SINS）在系泊条件下对准精度随非线性滤波状态维数增加而降低的问题，本文提出了一种基于卡尔曼滤波器（KF）和自适应尺度最小偏度单线采样无迹卡尔曼滤波器（ASMUKF）的混合滤波算法。进行了如下三点改进：（1）采用新的采样策略。为获得ASMUKF滤波算法，使用尺度最小偏度单线采样取代传统的对称采样方法，并将自适应尺度因子引入无迹卡尔曼滤波器（UKF）以校正实时变换采样过程；（2）将改进后的ASMUKF算法与KF相结合，形成KF-ASMUKF混合滤波模型；（3）将混合滤波模型分为线性部分和非线性部分。线性滤波部分采用KF滤波模型，非线性滤波部分采用ASMUKF模型。然后，本文设计了混合滤波算法的计算步骤。仿真和实验结果表明，所提出的混合滤波算法相对于传统算法具有一定优势，并且在系泊条件下能够减少船舶SINS的计算量并提高对准精度。
