Improving the Accuracy of Vehicle Position in an Urban Environment Using the Outlier Mitigation Algorithm Based on GNSS Multi-Position Clustering

In this paper, we propose a multi-position cluster-based weighted position estimation method that minimizes the influence of multipath (MP)/non-line-of-sight (NLOS) signals using a global navigation satellite system (GNSS) receiver. The proposed method is suitable for positioning passenger cars, particularly in urban driving environments. Density-based spatial clustering of applications with noise (DBSCAN)-based clustering is performed by generating multi-position data through a subset of observable satellites and analyzing the density characteristics of position data generated by line-of-sight (LOS) satellite signals from the generated multi-position data. To estimate the optimal position through clustered data, we propose a method by constructing a weighted model through Doppler-based velocity measurements, which is robust to MP delay signals compared to code-based pseudorange measurements. In addition, to prevent unnecessary clustering points from having weights, the predicted range is selected based on the nonholonomic constraint of the vehicle. The proposed algorithm was quantitatively validated by selecting a region in an actual urban environment where the MP/NLOS error could occur significantly. It was confirmed that the accuracy of vehicle position was improved by approximately 24% by the proposed method compared to existing positioning solutions.

Automated summary

In this paper, a multi-position cluster-based weighted position estimation method is proposed to mitigate the effects of multipath/non-line-of-sight signals using a GNSS receiver. The method is suitable for positioning passenger cars in urban driving environments. Density-based spatial clustering of applications with noise (DBSCAN) is used to analyze the density characteristics of position data generated by line-of-sight signals. By constructing a weighted model using Doppler-based velocity measurements, the proposed method improves the accuracy of vehicle positioning by approximately 24% compared to existing solutions.