针对惯性导航系统受模型误差和测量异常值误差的影响,姿态解算结果易出现精度差甚至发散的问题,提出了一种基 于平方根容积卡尔曼滤波(square-root cubature Kalman filter,SRCKF)w-检测的多传感器姿态融合算法。 利用协方差匹配法对 SRCKF 的新息序列进行自适应调整,经过调整后的新息在迭代过程中会补偿量测噪声方差阵,减小模型误差影响;再利用调整 后的新息进行误差探测,提高 w-检测的探测精度,并构造观测值替换准则进行误差观测值替换,解决测量异常值误差带来的影 响;最后利用 SRCKF 进行姿态融合,陀螺仪的姿态作为状态方程,经检测替换后的加速度计和磁力计姿态作为量测方程。 实验 表明,所提算法可以准确估计系统姿态,与传统算法相比解算精度平均可提升 62. 43%,在不同条件下,算法整体性能均可得到 大幅提升,并能快速进行姿态解算,保证解算精度。
In order to solve the problem that the filtering accuracy is poor or even divergent when the inertial navigation system is affected by model errors and measurement outlier errors, a multi-sensor fusion algorithm based on square-root cubature Kalman filter(SRCKF)wdetection is proposed. The square-root cubature Kalman filter innovation sequence is adaptively adjusted by the covariance matching method, and the adjusted innovation will compensate the measurement noise variance matrix and reduce the influence of model error, then use the adjusted innovation to detect the error and improve the w- detection accuracy. And construct the observation value replacement criterion to replace the error observation value, so as to solve the influence of the measurement abnormal value error. Finally, the attitude fusion is carried out by using SRCKF, the attitude of the gyroscope is taken as the state equation, and the attitude of the accelerometer and magnetometer replaced by detection is taken as the measurement equation. The experimental results show that the proposed algorithm can accurately estimate the system attitude, and the average solution accuracy can be improved by 62. 43% compared with the traditional algorithm. Under different conditions, the overall performance of the algorithm can be greatly improved, and the attitude can be solved quickly to ensure the solution accuracy.