Application of Improved Genetic Algorithm Based on Voronoi Partitioning in Pseudolite Deployment for Tunnel...

Area of Science:

• * Navigation and Positioning Systems
• * Railway Engineering
• * Signal Processing

Background:

• * Global Navigation Satellite System (GNSS) signals are unreliable in tunnels due to blockage and multipath.
• * Precise train positioning is critical for intelligent operations and safety monitoring.
• * Existing methods often optimize average geometric quality, neglecting worst-case scenarios.

Purpose of the Study:

• * To develop a deployment-oriented numerical framework for optimizing pseudolite layouts in tunnels.
• * To enhance positioning reliability by minimizing worst-case geometric degradation along train trajectories.
• * To provide a practical toolchain for designing pseudolite infrastructure in underground environments.

Main Methods:

• * Established a high-fidelity 3D tunnel-train model to assess line-of-sight (LoS) availability and occlusion.
• * Implemented a Voronoi-partition-constrained improved genetic algorithm (IGA) for pseudolite deployment.
• * Minimized the 90th-percentile PDOP (qPDOP) to address tail-risk geometric degradation and incorporated engineering constraints.

Main Results:

• * Achieved more uniform pseudolite coverage and improved full-trajectory geometric stability.
• * Significantly reduced high-quantile PDOP and mitigated local positioning spikes in occlusion-sensitive areas.
• * Demonstrated effectiveness under cost-constrained, sparse deployment scenarios.

Conclusions:

• * The proposed framework offers a practical solution for pseudolite infrastructure design in tunnels.
• * Minimizing qPDOP effectively suppresses positioning errors under challenging geometric conditions.
• * The method ensures robust and reliable high-precision positioning for intelligent railway systems.