基于改进粒子滤波的农用车辆导航定位方法

针对农业机械导航系统中常用的Kalman滤波对多传感器数据进行融合的算法不适用于非线性农用车辆导航系统的问题，该文采用粒子滤波方法进行数据融合，以获得准确的导航定位信息；该算法增加抗野值步骤，有效削弱GPS跳变引起的误差；通过对重要密度函数进行改进，引入无迹卡尔曼滤波方法（UKF），并采用不同重采样方法，有效抑制了粒子退化；增加MCMC步骤，减少了样本枯竭现象。仿真结果表明，改进后粒子滤波方法，可有效提高精度，减小导航误差，可满足农用车辆与作业机械的导航要求。

Positioning method of vehicle navigation system based on improved particle filter

In agriculture machinery navigation system, Kalman filter is widely used, but it is not good for non-linear system. An improved particles filter methodology used in data fusion was discussed to fuse the data for the better position information. The method involves three parts, the first is anti-outlie step to reduce the error effectively made by GPS; the second is to induct the UKF as the important function, and resample to avoid the degradation; the third is to use MCMC step to reduce the sample impoverishment. The simulation results show that the improved particle filter method can increase the accuracy and reduce the position error. The method can meet the requirements of agriculture machinery navigation.