UAV Aided Vehicle Positioning with Imperfect Data Association

Liang, Tianhao; Yang, Jiayan; Zhang, Tingting

doi:10.1109/vtc2021-spring51267.2021.9448650

Cited by 7 publications

(1 citation statement)

References 11 publications

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“…In addition, this trajectory data-based position estimation technique has limitations in estimating positions for non-empirical path data because similar trajectories are clustered from a temporal or spatial point of view, and a large amount of trajectory data are required to estimate the position accurately. Although prior studies have been conducted on cluster-based localization methods using the similarity of observation data, there are few studies considering MP/NLOS signals in urban environments through position trajectories and position measurements [27][28][29][30][31][32]. In addition, it is difficult to prove the efficiency of these urban environments position methods solely through simulators.…”

Section: Related Workmentioning

confidence: 99%

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

Kim,

Lee

et al. 2023

Remote Sensing

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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.

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