改进支持向量机的车辆定位导航精度提升方法

Research on accuracy improvement of vehicle positioning and navigation with an improved support vector machine

车辆定位导航是实现智能车辆环境感知的基础,为解决智能车辆在SINS/GPS组合导航下误差问题,提出一种基于蚁群算法改进支持向量机的车辆定位导航精度提升方法。首先,使用状态变换扩展卡尔曼滤波对组合导航进行初步降噪;其次,运用支持向量机及神经网络辅助导航,解决组合导航中位置误差较大、对导航效果产生影响的问题;然后,通过蚁群算法改进支持向量机,对支持向量机核函数参数进行迭代优化;最后,在实车采集数据集下,与神经网络辅助进行对比。结果表明:在东北天3个方向上,神经网络降低误差均方根值的效果达到了72.88%、68.66%、63.87%,而改进支持向量机的效果达到了82.09%、79.62%、90.14%。改进支持向量机能够辅助优化组合导航位置误差,提升车辆定位导航精度。

Vehicle positioning and navigation is the basis of realizing environment perception of intelligent vehicles.To solve the error problem of intelligent vehicles under SINS/GPS integrated navigation,this paper proposes a method of improving vehicle positioning and navigation accuracy based on an improved support vector machine with ant colony algorithm.Firstly,an extended Kalman filter with state transformation is proposed to reduce noise of the integrated navigation system.Secondly,the support vector machine and the neural network aided navigation are proposed to solve the problem of large position error and the influence on navigation effect in the integrated navigation.Then,the support vector machine is improved by ant colony algorithm,and the kernel function parameters of the support vector machine are optimized iteratively.Finally,it is compared with the neural network assistance in the real vehicle collection data set.The results show that the neural network can reduce the root mean square value of error by 72.88%,68.66%and 63.87%in the three directions of east,north and up(ENU),while the improved support vector machine can achieve 82.09%,79.62%and 90.14%.The improved support vector machine can help optimize the position error of the integrated navigation and improve the accuracy of vehicle positioning and navigation.