Zair Salim, Le Hégarat-Mascle Sylvie, Seignez Emmanuel
SATIE (Systems Applications of Information Energy Technologies) laboratory, University of Paris-Sud, 91405 Orsay, France.
Sensors (Basel). 2016 Apr 22;16(4):580. doi: 10.3390/s16040580.
In urban areas or space-constrained environments with obstacles, vehicle localization using Global Navigation Satellite System (GNSS) data is hindered by Non-Line Of Sight (NLOS) and multipath receptions. These phenomena induce faulty data that disrupt the precise localization of the GNSS receiver. In this study, we detect the outliers among the observations, Pseudo-Range (PR) and/or Doppler measurements, and we evaluate how discarding them improves the localization. We specify a contrario modeling for GNSS raw data to derive an algorithm that partitions the dataset between inliers and outliers. Then, only the inlier data are considered in the localization process performed either through a classical Particle Filter (PF) or a Rao-Blackwellization (RB) approach. Both localization algorithms exclusively use GNSS data, but they differ by the way Doppler measurements are processed. An experiment has been performed with a GPS receiver aboard a vehicle. Results show that the proposed algorithms are able to detect the 'outliers' in the raw data while being robust to non-Gaussian noise and to intermittent satellite blockage. We compare the performance results achieved either estimating only PR outliers or estimating both PR and Doppler outliers. The best localization is achieved using the RB approach coupled with PR-Doppler outlier estimation.
在城市地区或存在障碍物且空间受限的环境中，使用全球导航卫星系统（GNSS）数据进行车辆定位会受到非视距（NLOS）和多径接收的阻碍。这些现象会产生错误数据，干扰GNSS接收器的精确定位。在本研究中，我们检测观测值、伪距（PR）和/或多普勒测量中的异常值，并评估剔除这些异常值如何改善定位。我们为GNSS原始数据指定了一种反例建模方法，以推导一种将数据集划分为内点和异常值的算法。然后，在通过经典粒子滤波器（PF）或 Rao-Blackwellization（RB）方法执行的定位过程中，仅考虑内点数据。这两种定位算法都仅使用GNSS数据，但在处理多普勒测量的方式上有所不同。我们在一辆车上搭载GPS接收器进行了一项实验。结果表明，所提出的算法能够检测原始数据中的“异常值”，同时对非高斯噪声和间歇性卫星遮挡具有鲁棒性。我们比较了仅估计PR异常值或同时估计PR和多普勒异常值所取得的性能结果。使用RB方法结合PR-多普勒异常值估计可实现最佳定位。
