复杂观测环境下GNSS信号分类及精度评估

GNSS Signal Classification and Accuracy Evaluation in Complex Observation Environment

基于高度角、信噪比和伪距残差3个指标,采用K均值(Kmeans^(++))、迭代自组织数据分析法(ISODATA)和基于密度带有噪声的空间分类法(DBSCAN)对复杂城市观测环境下的GNSS数据进行分类,并采用伪距单点定位模型(SPP)评估不同算法的分类精度。结果表明,Kmeans^(++)算法分类精度最优,在E、N、U 3个方向上的定位精度分别达2.56 m、3.25 m、9.73 m;相较于未采用Kmeans^(++)算法的定位精度分别提升57.86%、47.64%、60.98%。为进一步验证算法性能,将Kmeans^(++)算法与信噪比、高度角阈值法进行精度对比,结果表明,Kmeans^(++)算法的平面和三维定位精度均有显著改善,分别提升24.87%、39.07%(信噪比阈值法)和41.36%、59.91%(高度角阈值法)。

Based on the three indicators of altitude angle,signal-to-noise ratio and pseudorange residual,this paper adopts K-means(Kmeans^(++)),iterative self-organizing data analysis method(ISODATA)and density-based spatial classification with noise(DBSCAN)to classify the GNSS data in complex urban observation environments.We evaluate the classification accuracy of different algorithms using pseudorange single point positioning(SPP).The results show that the Kmeans^(++) algorithm has the best classification accuracy.The accuracy of positioning in three directions of E,N and U is 2.56 m,3.25 m,and 9.73 m respectively;compared with not using the Kmeans^(++) algorithm,the positioning accuracy is improved by 57.86%,47.64%,and 60.98%.To further verify the performance of the algorithm,the accuracy of the Kmeans^(++) algorithm is compared with the signal-to-noise ratio and height angle threshold algorithm.The results show that the plane and three-dimensional positioning accuracy of the Kmeans^(++) algorithm is significantly improved by 24.87%,39.07%(signal-to-noise ratio algorithm)and 41.36%,59.91%(height angle threshold algorithm),respectively.