Our paper is accepted by IEEE Transactions on Intelligent Transportation Systems

It is great to share that our paper (“3D LiDAR Aided GNSS NLOS Correction by Direction-of-Arrival Estimation Using Doppler Measurements in Urban Canyons ”, by Xikun Liu, Weisong Wen, Liyuan Zhang, and Li-Ta Hsu) is accepted by the IEEE Transactions on Intelligent Transportation Systems. Congratulations to Xikun and our collegues.

Abstract

Global navigation satellite system (GNSS) positioning in urban environments suffers from significant accuracy degradation due to non-line-of-sight (NLOS) signal receptions. Existing correction methods, such as 3D model-aided and 3D LiDAR-aided GNSS, lack signal direction information and typically construct candidate reflection paths by exhaustively searching over possible reflection surfaces or azimuth angles, and selecting the final path based on the shortest-path assumption. However, this assumption is often invalid in dense urban canyons. To address this limitation, we propose a novel GNSS NLOS correction method that uses Doppler shift measurements to infer signal directional information, which is integrated with real-time point cloud mapping to reconstruct the actual signal reflection path actively. This approach allows us to directly track signal reflection, eliminating the need for exhaustive candidate generation and the shortest-path assumption. Experiments conducted on datasets collected in urban canyons demonstrate the effectiveness of the proposed method. Results show that the method achieves over 90% correction availability for NLOS signals, leading to more than 50% improvement in 3D GNSS positioning accuracy.

System Framework

Demonstration for GNSS NLOS correction