摘要
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。
Aiming at the positioning issue for maneuverable land vehicle, a filter model and new nonlinear filter algorithm for GPS/DR integrated positioning system based on SR-UKF was presented. For the system characteristic of linear state eqation and nonlinear measurement equation, the nonlinear filter algorithm which combines SR-KF and SR-UKF was proposed. In the phase of time updating, the operation time used for new algor/thm is much less than for general SR-UKF. The efficiency of computation was improved. The simulation result based on experimental data demonstrates that the new algorithm has better filtering precision and stability than EKF, and reduces computing time compared with general SR-UKF. The new algorithm is fit for the navigation system of land vehicle.
出处
《系统仿真学报》
CAS
CSCD
北大核心
2009年第3期721-723,742,共4页
Journal of System Simulation
基金
基于视觉信息的环境感知与目标识别关键技术(2007CB311005)
作者简介
杨静（1976-），女，西安，讲师，博士生，研究方向非线性滤波、智能车辆组合导航；
郑南宁（1952-），男，西安，博导，研究方向模式识别、人工智能与计算机视觉等领域的应用基础理论和工程技术。
