Vehicle-mounted GNSS navigation and positioning algorithm considering signal obstruction and fuzzy logic in urban environment

Abstract

In response to the challenges of limited navigation and positioning accuracy in urban environments and insufficient utilization of Non-Line-of-Sight (NLOS) information, this paper proposes a vehicle-mounted GNSS navigation and positioning algorithm considering signal obstruction and fuzzy logic in urban environment. Initially, the environment information above the vehicle is captured with the assistance of the skyward fisheye camera. Based on the designed image segmentation technology, the sky and non-sky areas are distinguished. Subsequently, a satellite projection model is established to project satellite signals onto the obtained image, by comparing the edge pixel coordinates of the sky and non-sky areas, Line of Sight (LOS) and NLOS signals are differentiated. Furthermore, addressing the issue that existing research has not fully characterized the obstruction degree of NLOS signals, a method is proposed to represent the degree of obstruction of satellite NLOS signals based on the principle of the nearest pixel point and to reasonably utilize their useful information. Building on this, combining the obtained representation of satellite NLOS signal obstruction, elevation angle, and Signal-to-Noise Ratio (SNR), a weighted comprehensive assessment method is designed based on the characteristics of fuzzy logic in handling uncertainty and ambiguity, thereby fully and reasonably utilizing NLOS signal information. Finally, this method improves positioning accuracy by 43.61% and 39.15% compared to existing SNR/elevation-based and NLOS-discarding methods, respectively, and by 7.09% over the best results achieved with uniform weighting of NLOS signals. It also offers greater efficiency and robustness than methods relying on average pixel values to assess NLOS signals.