基于经验模态分解去噪提高GPS定位精度

Improving GPS Positioning Accuracy Based on Empirical Mode Decomposition

在GPS精密定位中,多路径效应等无法建模的误差严重影响着定位结果的精度。这类误差不能通过传统的建模方式进行处理,也不能通过数据组合进行消除,更不能作为参数进行估计。因此本文基于经验模态分解提出一种数据驱动的噪声消除策略,将坐标时间序列分解为许多不同频率组成的时间序列,根据需要重构原始时间序列。并采用了仿真实验和实测数据验证所提出方法的有效性,结果显示所提出的方法可以有效的消除多路径效应等无法建模误差的影响。

In GPS precise positioning,errors such as multipath effect,which cannot be modeled,seriously affect the accuracy of positioning results.Such errors cannot be processed by traditional modeling methods,cannot be eliminated by data combination,and cannot be estimated as parameters.Therefore,this paper proposes a data-driven noise cancellation strategy based on empirical mode decomposition.The coordinate time series is decomposed into many time series with different frequencies,and the original time series is reconstructed according to the need.The validity of the proposed method is verified by simulation experiments and measured data.The results show that the proposed method can effectively eliminate the influence of unmodeled errors such as multipath effect.