三种GPS定位优化算法的实现及比较

Implementation and validity of three GPS positioning optimization algorithms

为进一步提高GPS定位精度,分析了目前研究中主要采用的加权最小二乘法和卡尔曼滤波两种优化算法。在此基础上,提出了一种利用粒子滤波器对伪距误差进行修正的定位优化算法。粒子滤波算法的特点是可直接用于求解非线性问题,对非高斯的误差进行修正。通过GPS实测数据进行实验,分别对这三种算法进行验证并分析了其各自特点。实验结果显示,三种算法均能使GPS定位精度得以明显改善。提出的粒子滤波算法作为一种新的GPS定位优化算法,在卫星导航定位领域中有着较好的参考价值。

Two conventional optimization algorithms （Weighted Least Squares Method and Kalman Filters Algorithm） were analyzed to further improve GPS positioning accuracy. Based on the analysis, a Particle Filter Algorithm for GPS positioning optimization by compensating the pseudorange error was presented. The advantage of particle filter is that the system isn＇t required to be linear, and can be used for resolving nonlinear problem and compensating the non-Gaussian error. The effectiveness of these three GPS positioning optimization algorithms were validated by experiments on GPS real-received data, and their characteristics were analyzed. The results show that all these three algorithms can significantly improve GPS positioning accuracy. As a novel GPS positioning optimization method, the proposed Particle Filter Algorithm has good reference value in navigation field.