基于UKF的UWB和GPS融合定位算法

Fusion Positioning Algorithm of UWB and GPS Based on UKF

智能汽车的发展对高精度定位需求日益显现.针对汽车在城市建筑群、立交桥等特定环境下,可见GPS卫星数量下降、车载GPS和惯性测量单元(inertial measurement unit,IMU)组合定位系统中IMU产生积累误差导致不能精确定位问题,本文提出一种基于无迹卡尔曼滤波(unscented Kalman filter,UKF)的超宽带(ultra wide band,UWB)和GPS融合定位算法.通过构建系统方案,优化UWB模块数据解析算法,构建UWB和GPS非线性融合系统模型,分析算法复杂度,将算法写入控制器进行实时滤波,对不同算法下的噪声误差和方差进行数据分析.实验表明基于无迹卡尔曼滤波的UWB和GPS融合定位算法实时性好、解算精度高、无滤波发散现象,可满足车辆在城市特定环境下高精度定位需求.

The demand of intelligent vehicles for high-precision positioning is increasingly strong.In specific environments of urban buildings,overpasses,and so on,the number of visible GPS satellites decreases and the inertial measurement unit(IMU)in a fusion positioning system of the vehicle GPS and the IMU produces a time accumulation error,leading to inaccurate positioning.This study proposes a fusion positioning algorithm of an ultra wide band(UWB)and a GPS based on the unscented Kalman filter(UKF).The system architecture scheme is constructed.The data analysis algorithm for the UWB module is optimized,and the model of a nonlinear fusion positioning system of a UWB and a GPS is built.The complexity of the algorithm is analyzed,and the algorithm is written into the controller for real-time filtering.The noise error and variance of different algorithms are analyzed.The experiments show that the fusion positioning algorithm of a UWB and a GPS based on the unscented Kalman filter,with good real-time performance,high solution accuracy,and no filter divergence,can meet the needs of high-precision positioning of vehicles in specific urban environments.