基于自适应CKF的姿态数据融合算法

为了提高基于MEMS惯性传感器的捷联惯性导航系统姿态解算的精度，提出了一种自适应容积卡尔曼滤波（CKF）数据融合算法。该数据融合算法将姿态四元数作为系统状态，将加速度计信息和磁力计信息作为系统观测量，对系统过程噪声矩阵和观测噪声矩阵进行实时的自适应估计，解决了因系统噪声突变引起的姿态解算精度急剧下降的问题。实验结果表明，采用自适应CKF数据融合算法比单纯基于陀螺仪的捷联姿态解算精度有明显的提高，在载体动态时测得的横滚角和俯仰角误差在1°以内，航向角误差在2°以内。

Attitude data fusion algorithm based on adaptive CKF

In order to improve the precision of attitude calculation of strapdown inertial navigation system based on MEMS inertial sensor, an adaptive cubature Kalman filter (CKF) data fusion algorithm is proposed. The data fusion algorithm takes the attitude quaternion as the system state, uses the accelerometer information and the magnetometer information as the system observation, and performs real-time adaptive estimation on the system process noise matrix and the observed noise matrix to solve the problem of rapid descent of attitude calculation accuracy caused by the sudden change of system noise. The experimental results show that the adaptive CKF data fusion algorithm is significantly better on calculation accuracy than the gyroscope-based strapdown attitude calculation. The roll and pitch angle errors measured in the carrier dynamics are within 1° and the heading angle error is within 2°.