基于改进容积卡尔曼滤波的惯性/光流组合自主测速方法

针对容积卡尔曼滤波算法在惯性/光流组合测速数据融合时出现由于各系统输出数据频率不一致导致融合精度有限的问题,提出了一种基于多速率残差校正的改进容积卡尔曼滤波算法.通过当前时刻误差估算组合导航系统残差,再使用估算后的残差对速度估计值进行补偿,最终实现惯性/光流组合系统速度测量值的数据融合.实验结果表明,通过提出的改进容积卡尔曼滤波对惯性/光流数据进行融合后,东向速度均方根误差为0.2964m/s,北向速度均方根误差为0.06m/s,与现有其他卡尔曼滤波算法相比,此方法可显著提高惯性/光流组合系统的速度测量精度.

Inertial/Optic Flow Combined Autonomous Velocity Measurement Based on Improved Cubature Kalman Filtering

To solve the problem of limited fusion accuracy due to the inconsistency of the output data frequency of each system in the fusion of inertial/optical flow combined velocity measurement data by cubature Kalman filter algorithm, an improved cubature Kalman filter algorithm based on multi-rate residual correction is proposed. The combined navigation system residuals are estimated from the current moment errors, and then are used to compensate for the velocity estimates, and finally the data fusion of the velocity measurements of the inertial/optical flow combined navigation system is achieved. The experimental results show that the mean square error of eastward velocity is 0.2964m/s and that of northward velocity is 0.06m/s after the fusion of inertial/optical flow data by the improved cubature Kalman filter proposed in this paper. This method leads to a significant improvement in the accuracy of velocity measurements for inertial/optical flow combined navigation compared to other existing Kalman filtering algorithms.