基于高分值加权的改进阴影匹配定位算法研究

Improved Shadow Matching Positioning Algorithm Based on High Score Weighting

针对传统阴影匹配(SM)信噪比阈值不能有效区分LOS/NLOS的情况,使卫星可见性观测不准导致较大的定位误差,本文在分析城市峡谷中接收机接收的信噪比特性基础上,提出了一种基于高分值加权的改进SM定位算法,并使用粒子滤波对动态场景下的改进SM定位结果进行滤波。实验结果表明,静态和动态场景下该算法的平均定位误差为2.45m和4.64m,相对于传统SM定位算法的3.96m和5.95m,分别降低了38.1%和21.9%.

Traditional shadow matching signal-to-noise ratio （SNR） threshold cannot distinguish LOS/NLOS effectively, resulting in larger positioning error because of inaccurate visibility of the satellites. Based on the analysis of the SNR characteristics received by the receiver in the urban canyon, an improved shadow matching algorithm based on high score weighting is proposed, and the results of improved algorithm are filtered by particle filterin the dynamicscene. Experimental results show that the average positioning error of the algorithm is 2.45 m and 4.64 m in static and dynamic scene, which is reduced by 38.1% and 21.9% compared to 3.96 m and 5.95 m of traditional shadow matchingpositioning algorithm.