测绘通报 ›› 2026, Vol. 0 ›› Issue (5): 117-121,148.doi: 10.13474/j.cnki.11-2246.2026.0519

• 学术研究 • 上一篇    

高空环境下GNSS+INS导航定位方法研究

王杨生, 冯雅欣, 刘华英, 薛剑   

  1. 山东东方道迩数字数据技术有限公司, 山东 济南 250000
  • 收稿日期:2025-10-30 发布日期:2026-06-09
  • 作者简介:王杨生(1985—),男,中级工程师,主要研究方向为测绘地理信息。E-mail:545608621@qq.com

Research on GNSS+INS navigation and positioning method in high-altitude environments

WANG Yangsheng, FENG Yaxin, LIU Huaying, XUE Jian   

  1. Eastdawn Digital Data Technology Co., Ltd., Jinan 250000, China
  • Received:2025-10-30 Published:2026-06-09

摘要: [目的]在树木茂密的山区环境中,BDS与GPS的系统间偏差(ISB)会显著增大全球导航卫星系统(GNSS)的定位误差,导致其难以为惯性导航系统(INS)提供高精度的位置修正信息。这进一步造成GNSS+INS组合导航中的定位误差不断累积,严重影响无人机电力巡检的性能。[方法]为解决上述问题,本文提出一种考虑BDS与GPS系统间偏差的动态自适应卡尔曼滤波算法。在GNSS+INS动态自适应融合的基础上,构建了BDS+GPS组合定位ISB的量测方程,并引入GNSS检核策略剔除观测粗差,从而显著提升状态估计的精度与稳健性。[结果]试验结果表明,利用本文算法能够有效提高GNSS+INS位置估计性能,水平方向定位结果的RMSE小于10 cm,高程方向的RMSE小于8 cm,定位性能明显优于EKF和AKF算法。[结论]可以为无人机电力巡检系统提供设计参考依据。

关键词: ISB, GNSS+INS, 动态自适应卡尔曼滤波, 电力巡检系统

Abstract: [Purposes] In densely forested mountainous environment,inter-system bias(ISB)between BDS and GPS significantly increases global navigation satellite system (GNSS)positioning errors.This prevents GNSS from providing high-precision position corrections to the inertial navigation system (INS).Consequently,positioning errors accumulate in GNSS+INS integrated navigation,severely degrading the performance of UAV power line inspection.[Methods] To address this issue,this paper proposes a dynamic adaptive Kalman filtering algorithm that accounts for BDS+GPS ISB.A measurement equation for BDS and GPS ISB in integrated positioning is established based on GNSS+INS dynamic adaptive fusion.A GNSS quality-check strategy is introduced to eliminate observation outliers,thereby significantly improving the accuracy and robustness of state estimation.[Findings]Experimental results demonstrate that the proposed ISB dynamic Kalman filter effectively enhances GNSS+INS position estimation performance.The RMSE are less than 10 cm horizontally and below 8 cm vertically,outperforming both the EKF and the AKF.[Conclusions]These findings provide a valuable design reference for UAV power inspection systems.

Key words: ISB, GNSS+INS, dynamic adaptive Kalman filter, power inspection system

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