考虑里程计截断误差的SINS/OD组合导航算法

SINS/OD integrated navigation algorithm considering odometer truncation error

针对车载惯导/里程计组合导航系统中,里程计测量脉冲输出只为整数,存在截断误差的问题,提出了3种考虑里程计截断误差补偿的SINS/OD组合导航算法。首先,在传统的速度匹配组合导航基础上,将里程计截断误差作为系统状态变量,建立了基于速度观测的考虑截断误差的卡尔曼滤波导航算法;其次,为了降低噪声,不改变系统状态量,将捷联惯导输出转化为脉冲输出与里程计脉冲输出做差作为量测值,建立了基于脉冲观测的卡尔曼滤波导航算法;最后,针对随机常值模型估计里程计截断误差的局限性,提出基于高斯回归模型的里程计截断误差预测和对观测值进行补偿的方法,以实现组合导航解算。140多公里的车载实验结果表明,基于脉冲观测和基于高斯回归模型的算法相比传统算法在定位精度上均提升了80%以上。

To solve the problem of truncation error in vehicle-mounted inertial navigation/odometer integrated navigation system,three SINS/OD integrated navigation algorithms considering truncation error compensation of odometer are proposed.First,on the basis of the traditional speed matching integrated navigation method,the truncation error of the odometer is used as the system state variable,and a Kalman filter navigation algorithm for the truncation error based on the speed observation is established.Secondly,in order to reduce the noise and not change the system state quantity,the strapdown inertial navigation system output is converted into pulse output.The difference between the pulse output converted by INS output and the odometer pulse output is taken as the measurement value,and a Kalman filter navigation algorithm based on pulse observation is established.Finally,aiming at the limitation of estimation of odometer truncation error by stochastic constant value model,a method of odometer truncation error prediction and compensation of observation value based on Gaussian regression model is proposed.The experiment results of vehicle-mounted for more than 140 kilometers show that the positioning accuracy of the algorithm based on pulse observation and the algorithm based on Gaussian regression model are improved by more than 80%compared with the traditional algorithm.