Sensors, Vol. 24, Pages 2611: A Post-Processing Multipath/NLoS Bias Estimation Method Based on DBSCAN

Sensors, Vol. 24, Pages 2611: A Post-Processing Multipath/NLoS Bias Estimation Method Based on DBSCAN Sensors doi: 10.3390/s24082611 Authors: Yihan Guo Simone Zocca Paolo Dabove Fabio Dovis Positioning based on Global Navigation Satellite Systems (GNSSs) in urban environments always suffers from multipath and Non-Line-of-Sight (NLoS) effects. In such conditions, the GNSS pseudorange measurements can be affected by biases disrupting the GNSS-based applications. Many efforts have been devoted to detecting and mitigating the effects of multipath/NLoS, but the identification and classification of such events are still challenging. This research proposes a method for the post-processing estimation of pseudorange biases resulting from multipath/NLoS effects. Providing estimated pseudorange biases due to multipath/NLoS effects serves two main purposes. Firstly, machine learning-based techniques can leverage accurately estimated pseudorange biases as training data to detect and mitigate multipath/NLoS effects. Secondly, these accurately estimated pseudorange biases can serve as a benchmark for evaluating the effectiveness of the methods proposed to detect multipath/NLoS effects. The estimation is achieved by extracting the multipath/NLoS biases from pseudoranges using a clustering algorithm named Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The performance is demonstrated using two real-world data collections in multipath/NLoS scenarios for both...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research