摘要
To improve the accuracy of the calculation of a heading angle under magnetic interference,magnetometers and inertial measurement units(IMUs)were fused.The observation value of the heading angle was deduced on the basis of the modeling of the magnetometer error and the analysis of the relation of the magnetometer triaxial output and the distribution characteristics of the magnetic field at two adjacent time periods.Meanwhile,the gyro state and angular velocity increment were used as the basis of the IMU to calculate the prediction value of the heading angle.With the changes in the heading angle and environmental interference,a random forest(RF)algorithm was used to iteratively calculate the weights to fuse the observation value of the heading angle based on the magnetometer and the prediction value of the heading angle based on the IMU.The results show that relative to the common sensor fusion method,the proposed sensor fusion method based on the RF algorithm achieved an approximate 60%improvement in heading angle accuracy.Hence,the proposed method can effectively improve the accuracy of the heading angle under magnetic interference by using an RF algorithm to calculate the output weights of the magnetometer and IMU.
为了提高磁干扰环境下的航向角计算精度,将磁力计和惯性测量单元进行融合计算.在构建磁力计误差模型和分析磁力计三轴输出与相邻两时刻磁场分布特征关系的基础上,推导出航向角观测值.同时,采用陀螺状态和角速度增量作为惯性测量单元计算依据,计算出航向角的预测值.随着航向角和环境干扰的变化,使用随机森林算法持续迭代计算权重,将基于磁力计的航向角观测值和基于惯性测量单元的航向角预测值进行融合计算.结果表明,在磁干扰环境下,相比于普通的传感器融合方法,基于随机森林的传感器融合方法的航向角精度可提高约60%.通过随机森林算法计算合适的磁力计和惯性测量单元的输出权重,可有效提高磁干扰环境下的航向角计算精度.
基金
The National Natural Science Foundation of China(No.51708299).
作者简介
Wang Dongsheng(1983—),male,doctor,associate professor,wangdongsheng@njupt.edu.cn.
