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A New Ground-Based Pseudolite System Deployment Algorithm Based on MOPSO.

Wenjie Tang1,2, Junping Chen1,2, Chao Yu1,2

  • 1Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China.

Sensors (Basel, Switzerland)
|August 28, 2021
PubMed
Summary
This summary is machine-generated.

Optimal pseudolite deployment enhances ground-based system coverage and accuracy. A new multi-objective particle swarm optimization (MOPSO) algorithm improves signal reach and reduces the mean horizontal dilution of precision (HDOP).

Keywords:
dilution of precisionmulti-objective optimizationparticle swarm optimizationpseudolitevisibility

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Area of Science:

  • Geomatics Engineering
  • Navigation Systems
  • Optimization Algorithms

Background:

  • Pseudolite deployment is critical for ground-based navigation systems, directly impacting signal coverage and positioning accuracy.
  • Existing deployment methods often struggle to balance coverage and precision with limited pseudolite numbers.

Purpose of the Study:

  • To propose a novel multi-objective particle swarm optimization (MOPSO) algorithm for optimizing ground-based pseudolite system deployment.
  • To enhance signal coverage and minimize mean horizontal dilution of precision (HDOP) using limited pseudolites.

Main Methods:

  • Developed a MOPSO algorithm integrating Digital Elevation Model (DEM) data for geometric analysis.
  • Utilized mean HDOP of the DEM grid as a metric for system geometry.
  • Employed visual area analysis for efficient signal coverage calculation, avoiding complex intersection computations.

Main Results:

  • The proposed MOPSO algorithm achieved optimal pseudolite deployment in a 10 km×10 km test area in Jiuzhaigou, China.
  • Demonstrated significant improvements in coverage area (up to 50% more than existing PSO) and HDOP.
  • Showcased 30% greater coverage compared to the convex polyhedron volume optimization (CPVO) algorithm.

Conclusions:

  • The MOPSO algorithm offers a superior approach for ground-based pseudolite deployment, balancing coverage and positioning accuracy.
  • Integration of DEM data and efficient coverage calculation enhances deployment optimization for navigation systems.