Takayama Yoji, Urakubo Takateru, Tamaki Hisashi
Furuno Electric Co., LTD., Nishinomiya 662-0934, Japan.
Department of Information Science, Graduate School of System Informatics, Kobe University, Kobe 657-8501, Japan.
Sensors (Basel). 2021 Sep 9;21(18):6056. doi: 10.3390/s21186056.
One of the great unsolved GNSS problems is inaccuracy in urban canyons due to Non-Line-Of-Sight (NLOS) signal reception. Owing to several studies about the NLOS signal rejection method, almost all NLOS signals can be excluded from the calculation of the position. However, such precise NLOS rejection would make satellite geometry poor, especially in dense urban environments. This paper points out, through numerical simulations and theoretical analysis, that poor satellite geometry leads to unintentional performance degradation of the Kalman filter with a conventional technique to prevent filter divergence. The conventional technique is to bump up process noise covariance, and causes unnecessary inflation of estimation-error covariance when satellite geometry is poor. We propose a novel choice of process noise covariance based on satellite geometry that can reduce such unnecessary inflation. Numerical and experimental results demonstrate that performance improvement can be achieved by the choice of process noise covariance even for a poor satellite geometry.
全球导航卫星系统（GNSS）尚未解决的重大问题之一是，由于非视距（NLOS）信号接收，导致城市峡谷中的定位不准确。由于针对NLOS信号抑制方法开展了多项研究，几乎所有NLOS信号都可以在位置计算中被排除。然而，如此精确的NLOS抑制会使卫星几何图形变差，尤其是在密集的城市环境中。本文通过数值模拟和理论分析指出，较差的卫星几何图形会导致采用传统技术防止滤波器发散时，卡尔曼滤波器的性能出现意外下降。传统技术是提高过程噪声协方差，在卫星几何图形较差时会导致估计误差协方差不必要地增大。我们基于卫星几何图形提出了一种新颖的过程噪声协方差选择方法，该方法可以减少这种不必要的增大。数值和实验结果表明，即使在卫星几何图形较差的情况下，通过选择过程噪声协方差也可以实现性能提升。
