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针对智能车辆机动性运动的定位问题提出了一种基于平方根Unscented卡尔曼滤波的GPS/DR组合定位方案和算法。基于组合定位模型状态方程线性和观测方程非线性的特点,提出了将标准平方根卡尔曼滤波同SR-UKF相结合的非线性滤波算法。该算法在时间更新阶段减少了滤波算法的运算量,提高了滤波算法的效率。仿真结果表明:与基于EKF的非线性滤波算法相比,本算法具有更高的滤波精度和更好的滤波稳定性,同时,同通用SR-UKF相比又具有较高的运算效率,完全适合于资源有限的车载导航系统。
In order to solve the problem of locomotion of smart vehicles, a GPS / DR combined positioning scheme and algorithm based on square root Unscented Kalman filter is proposed. Based on the linearity of the state equation of the combined positioning model and the nonlinearity of the observation equation, a nonlinear filtering algorithm combining standard square root Kalman filter with SR-UKF is proposed. The algorithm reduces the computational complexity of the filtering algorithm in the time updating stage and improves the efficiency of the filtering algorithm. The simulation results show that the proposed algorithm has higher filtering accuracy and better filtering stability than the nonlinear filtering algorithm based on EKF, and at the same time has higher computational efficiency compared with the common SR-UKF Car navigation system with limited resources.