基于平方根UKF的车辆组合导航

针对扩展卡尔曼滤波(EKF)在车辆导航中存在着计算复杂、线性化误差大等缺点,将一种新的非线性滤波方法--平方根UKF方法(SRUKF)用于车辆GPS/DR组合导航中.和普遍采用的EKF方法相比,SRUKF方法不仅提高了车辆组合定位的精度和稳定性;而且不需要模型的具体解析形式,避免了复杂的Jacobi-an矩阵的计算,算法更简单,也更加易于实现.为了检验其有效性,将两种方法分剐对车辆GPS/DR组合导航系统进行滤波仿真,仿真结果进一步表明SRUKF方法明显优于EKF方法,是车辆组合导航中一种更理想的非线性滤波方法,真正实现了车辆低成本、高精度的实时定位.

Square-root unscented Kalman filter for vehicle integrated navigation

In view of that there exist some defects when the extended Kalman filter(EKF) is applied in the nonlinear state-estimation,the square-root unscented Kalman filter(SRUKF),as a new nonlinear filtering method,is introduced to instead of the EKF for the state-estimation of the vehicle integrated GPS/DR navigation system.Compared with the EKF,the SRUKF not only improves the location precision and algorithmic stability greatly,but also avoids the calculating burden of Jacobian matrices.This data fusion algorithm based on the SRUKF is easy to implement,and meets the requirements of low-cost and high precision.In order to test the validity of the SRUKF,the two methods are used to estimate states of the vehicle integrated GPS/DR navigation systems.The results of simulation show that the SRUKF is superior to the EKF and is a more ideal nonlinear filtering method for the vehicle integrated GPS/DR navigation.