Achieving Reliable Intervehicle Positioning Based on Redheffer Weighted Least Squares Model Under Multi-GNSS Outages

Area of Science:

• Vehicular technology
• Navigation systems
• Signal processing

Background:

• Reliable intervehicle positioning is essential for safety applications like collision avoidance and autonomous driving.
• Global Navigation Satellite System (GNSS) accuracy degrades in challenging environments, particularly during signal outages.
• Existing methods struggle with the varying degrees of inaccuracy and data loss during multi-GNSS outages.

Purpose of the Study:

• To propose a novel fusion technique for robust intervehicle positioning estimation.
• To address challenges posed by complete, partial, and free Global Navigation Satellite System (GNSS) pseudorange outages.
• To improve the reliability of positioning data for autonomous and connected vehicles.

Main Methods:

• Introduced non-Gaussian Redheffer weighted least squares (nGRWLS) for intervehicle positioning.
• Combined Gaussian dynamical matrix principle and Redheffer distribution for sparse data in complete outages.
• Utilized optimal window size for data flow regulation between Inertial Navigation Systems (INS) and GNSS in partial outages.
• Employed Generalized Error Distribution (GED) to model non-Gaussian noise in pseudorange and INS measurements.

Main Results:

• The nGRWLS method demonstrated accurate intervehicle positioning estimation across diverse GNSS outage scenarios.
• The approach effectively handled complete, partial, and free GNSS pseudorange outages.
• Experimental results validated the consistency and reliability of the proposed nGRWLS technique.

Conclusions:

• The proposed nGRWLS technique offers a reliable solution for intervehicle positioning under various GNSS outage conditions.
• This method enhances the robustness of navigation systems for safety-critical vehicular applications.
• The fusion approach provides accurate positioning crucial for the advancement of autonomous driving.