一种基于径向速度残差曲线拟合的车载SINS零速修正算法

The zero velocity update technology based on Kalman filter is one of the key technologies for land vehicle navigation. Aiming at the problems of short zero velocity update stop interval, sensitive measurement noise parameters, and the poor robustness of the filtering algorithm during dynamic zero velocity update (DZUPT) in traditional vehicle-mounted laser gyro strapdown inertial navigation, a composite zero velocity update algorithm combining adaptive Kalman filter and radial velocity residual curve-fitting was proposed. Based on the DZUPT algorithm, Sage-Husa adaptive Kalman filter and high-order curve-fitting were used to compensate the positioning error caused by the residual radial velocity of the vehicle, the dynamic and static zero velocity information is combined organically to further improve the overall positioning accuracy of the system. The vehicle-mounted experiments verify that under the condition of 15-minute zero velocity update interval, the accuracy of positioning has been reached 10 m(CEP).