基于LightGBM的GNSS多路径误差改正

GNSS multipath correction based on LightGBM

  • 摘要: 多路径误差是复杂环境下GNSS精密定位的主要误差源,严重限制其定位精度. 本文提出一种基于轻量级梯度提升机(light gradient boosting machine, LightGBM)的相位多路径改正方法. 该方法以卫星高度角、方位角、载噪比(carrier-to-noise ratio, CNR)作为输入特征,以静态后处理单差相位残差作为目标值,经训练后用于后续的多路径误差改正. 强多路径场景实验验证了所提算法的有效性,结果表明:相较于忽略多路径的传统定位方法,经LightGBM模型改正后的定位精度在北(N)、东(E)、高程(U)方向分别提升55.8%、47.0%和44.1%,模糊度固定率从29.4%提升至68.4%. 可见,LightGBM模型可有效捕捉多路径误差与所选数据特征间的非线性关联,为强多路径环境下GNSS高精度定位应用提供了可行的技术解决方案.

     

    Abstract: As a primary error source of GNSS precise positioning in complex environments, multipath error can adversely limit the positioning accuracy. This paper proposes a phase multipath correction method based on the light gradient boosting machine (LightGBM). The method adopts satellite elevation angle, azimuth angle, and carrier-to-noise ratio (CNR) as input features, uses the static post-processed single-difference phase residual as the target value, and then be applied to subsequent multipath error correction after training. To validate the effectiveness of the proposed algorithm, experiments were conducted in a multipath-strong environment. The results demonstrate that compared with traditional positioning method that ignores multipath error, after applying LightGBM-based multipath correction, the positioning accuracy is improved by 55.8%, 47.0%, and 44.1% in the north (N), east (E), and up (U) directions, respectively, and the ambiguity fixing rate is increased from 29.4% to 68.4%. It is concluded that the nonlinear correlation between multipath errors and the selected data features can be effectively captured by the LightGBM model, which provides a feasible solution for high-precision GNSS positioning applications in strong multipath environments.

     

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