GPS/SINS/偏振光组合导航的模糊控制算法研究

针对在组合导航中扩展卡尔曼滤波器(EKF)会发散的问题，研究了一种基于模糊控制的自适应卡尔曼滤波算法(FAKF)，通过模糊控制器来计算指数加权因子，实时对量测噪声的大小进行调整，保证滤波精度，有效抑制滤波过程的发散问题；针对GPS无法提供姿态角进行组合解算的情况，使用偏振光与SINS的组合进行姿态解算；本文通过对GPS/SINS/偏振光组合导航系统进行仿真，将基于模糊控制的自适应卡尔曼滤波算法(FAKF)和扩展卡尔曼滤波算法(EKF)进行对比验证，东、北、天向位置上分别提升精度56.81%、65.17%、45.99%，东、北、天向速度上提升精度46.99%、54.01%、43.82%，俯仰角、航向角分别提升精度58.01%、53.58%，证明了该方法的优势。

Research on fuzzy control algorithm for GPS/SINS/polarized light combined navigation

Aiming at the problem that the extended Kalman filter (EKF) will diverge in combined navigation, a fuzzy control-based adaptive Kalman filter algorithm (FAKF) is studied, which calculates the exponential weighting factor through a fuzzy controller to adjust the size of the measurement noise in real time to ensure the filtering accuracy and effectively inhibit the dispersion problem of the filtering process. Aiming at the situation that GPS cannot provide the attitude angle for combined solution, the combination of polarized light and SINS is used for attitude solution. In this paper, the fuzzy control based adaptive Kalman filter algorithm (FAKF) and extended Kalman filter algorithm (EKF) are compared and verified by simulating the GPS/SINS/polarized light combined navigation system, which improves the accuracy by 56.81%, 65.17%, and 45.99% on the position of east, north, and heading, respectively, and 46.99% on the speed of east, north, and heading, 54.01% and 43.82% on east, north and skyward velocity, and 58.01% and 53.58% on pitch angle and heading angle, respectively, which proves the advantages of the method.