摘要
设计了一种基于模糊规则调整的串级线性卡尔曼(LKF)姿态解算方法,用旋转矩阵部分元素建立状态方程首先以机动加速度补偿的加速度为观测量,并采用模糊规则调整不同运动状态下的协方差阵,减小加速度的干扰,得到水平姿态角;然后采用磁强信息和姿态信息获取间接观测量,得到偏航角。动静态测试表明,该方法消除了累计误差和磁干扰对水平倾角的耦合干扰,与扩展卡尔曼滤波(EKF)相比,提高了在运动加速度干扰和磁场干扰下的姿态估计精度,并且降低了计算量。
A cascaded Linear Kalman Filter（LKF） algorithm is designed based on fuzzy-rule. The state equations are set up by partial elements of the rotation matrix. Firstly, the measurement acceleration is compensated by motion acceleration, the covariance matrix is adjusted by the fuzzy rules in the different motion states to reduce the interference of acceleration, the horizontal attitude angles are obtained. Then the indirect observations are obtained by the magnetic and attitude information, the yaw is obtained. Dynamic and static tests show that this method can eliminate the accumulated error and reduce the influence of the magnetic interference on the horizontal angles. Compared with the Extended Kalman Filter, this method can improve the accuracy of attitude estimation and ease the computation.
出处
《电子技术应用》
北大核心
2017年第4期94-97,共4页
Application of Electronic Technique
关键词
姿态估计
加速度补偿
串级
解耦合
模糊规则
attitude estimate
acceleration compensation
cascaded
decoupled
fuzzy rules
