基于贝叶斯理论车辆定位几何精度因子求解

Algorithm to Solve the Geometric Dilution of Precision for Vehicle Cooperative Positioning Based on Bayes Theory

在车辆与全球卫星导航系统协同定位应用中,车辆与卫星的几何位置分布在很大程度上影响车辆的定位精度(几何定位精度因子);尤其是车车定位中车辆位置具有更大的随机性。针对几何精度因子求解过程中涉及矩阵求逆运算,提出了基于贝叶斯理论的几何精度求解算法。编程仿真表明,当求逆矩阵具有一定的稀疏度时,此算法在计算速度上一定程度优于传统的矩阵求逆算法(高斯消元法);同时,算法具有非常简短的编程实现,便于应用到接收机等低功耗设备中。

In the application of vehicle cooperative positioning with GNSS（global navigation satellite system）, different cars and satellites geometric positions named GDOP （ geometric dilution of precision） have a great effect on cars positioning precision especially for cars with highly random positions. Algorithm based on Bayes theory was presented for solving the solution of GDOP which matrix inversion is asked. Simulation results show the algorithm which proposed is better than that of traditional matrix inversion such as Gaussian elimination when matrix has some certain sparsity. Meanwhile, the algorithm has a simple programmatic implementation and is easily applied in the low power consumption receiver device.