孔德龙,刘 春,何 敏,汪志宁.GNSS 多系统 PPP 中强跟踪自适应Kalman 滤波的应用[J].电子测量与仪器学报,2023,37(10):24-31 |
GNSS 多系统 PPP 中强跟踪自适应Kalman 滤波的应用 |
Application of strong tracking adaptive Kalman filter in GNSS multi-system PPP |
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DOI: |
中文关键词: 精密单点定位 卡尔曼滤波器 自适应估计 渐消因子 测量噪声 |
英文关键词:PPP Kalman filtering adaptive estimation fading factor measurement noise |
基金项目:合肥市北斗卫星导航重大应用示范项目资助 |
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中文摘要: |
精密单点定位(precise point positioning, PPP)技术由于操作简单、定位精度高,现已广泛应用于许多领域。 针对 PPP 解
算过程中周围环境改变可能带来的观测噪声和多路径效应,传统滤波算法无法解决其导致的精度下降的问题,本文提出一种强
跟踪自适应 Kalman 滤波(strong tracking adaptative Kalman filtering, SAKF)算法,通过引入渐消因子调整预测误差值,同时使用
IGGⅢ函数方法重构测量噪声协方差,从而实现 PPP 解算。 实验结果表明,在静态解算时,SAKF 定位精度较传统算法提升约
20%,在仿动态解算时,SAKF 定位精度提升约 55% ~ 60%,同时具有更好的收敛稳定性。 |
英文摘要: |
Precise point positioning (PPP) technology has been widely used in many fields because of its simple operation and high
positioning accuracy. Aiming at the observation noise and multipath effect that may be caused by the change of surrounding environment,
the traditional filtering algorithm cannot solve the problem of precision decline caused by it, this paper proposes a strong tracking
adaptive Kalman filtering (SAKF) algorithm. The fading factor is introduced to adjust the prediction error value, and the measurement
noise covariance is reconstructed by IGGⅢ function method, to achieve realize PPP solution. The experimental results show that the
positioning accuracy of SAKF is improved by about 20% compared with the traditional algorithm in static solution, and it is improved by
about 55% ~ 60% in quasi-dynamic solution, and it has better convergence stability. |
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