In recent years, global navigation satellite systems (GNSSs) have been widely used in intelligent transport systems (ITSs), and many countries have been rapidly improving the infrastructure of their satellite positioning systems. However, there is a serious problem involving the use of kinematic GNSS positioning in urban environments, which stems from significant positioning errors introduced by non-line-of-sight (NLOS) satellites blocked by obstacles. Therefore, we propose the method for positioning based on NLOS satellites detection using a fish-eye camera. In general, it is difficult to robustly extract an obstacle region from the fish-eye image because the image is affected by cloud cover, illumination conditions, and weather conditions. We extract the obstacle region from the image by tracking image feature points in sequential images. Because the obstacle region on the image moves larger than the sky region, the obstacle region can be determined by performing image segmentation and by using feature point tracking techniques. Finally, NLOS satellites can be identified using the obstacle region on the image. The evaluation results confirm the GNSS positioning accuracy without the NLOS satellites was improved compared with using all observed satellites, and confirm the effectiveness of the proposed technique and the feasibility of implementing its highly accurate positioning capabilities in urban environments.
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Computer Science(all)