Satellite Positioning Accuracy Improvement in Urban Canyons Through a New Weight Model Utilizing GPS Signal Strength...

Area of Science:

• Geomatics Engineering
• Satellite Navigation Systems
• Signal Processing

Background:

• Urban environments degrade Global Positioning System (GPS) accuracy due to multipath interference and obstructed satellite visibility.
• Existing weighting models often struggle to mitigate these urban positioning challenges effectively.

Purpose of the Study:

• To introduce a novel HK model for enhancing real-time GPS positioning accuracy in urban settings.
• To leverage signal-to-noise ratio (SNR) variability for improved positioning without auxiliary sensors.

Main Methods:

• Developed the HK model, which classifies signals based on SNR standard deviation.
• Applied the model to 24-hour GPS datasets from open-sky, city street, and urban canyon environments.
• Compared the HK model's performance against conventional SNR- and elevation-based weighting techniques.

Main Results:

• Non-line-of-sight (NLOS) signals in urban areas show greater SNR variability than line-of-sight (LOS) signals.
• The HK model significantly improved 3D positioning accuracy, reducing horizontal Root Mean Square Error (RMSE) by up to 7.8m in urban canyons and 7.8m in city streets.
• Vertical RMSE in urban canyons decreased from 19.5m to 6.9m, and horizontal RMSE from 13.0m to 4.7m.

Conclusions:

• The HK model offers a substantial improvement over traditional methods for GPS positioning in challenging urban environments.
• Its effectiveness is particularly pronounced under severe multipath conditions common in dense urban settings.
• The model provides consistent accuracy enhancements, especially for vertical positioning.