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Factor Graph-Assisted Distributed Cooperative Positioning Algorithm in the GNSS System.

Chengkai Tang1,2, Lingling Zhang3, Yi Zhang4

  • 1School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China. cktang@nwpu.edu.cn.

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Summary
This summary is machine-generated.

This study introduces a factor graph-aided distributed cooperative positioning algorithm to enhance Global Navigation Satellite System (GNSS) accuracy for smart cities. The new method significantly improves positioning accuracy and reduces errors compared to existing techniques.

Keywords:
cooperative positioningdistributed positioningfactor graphstotal least squares

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Area of Science:

  • Navigation and Positioning Systems
  • Smart City Technologies
  • Geomatics Engineering

Background:

  • Smart city development demands higher accuracy in navigation and positioning services.
  • Existing Global Navigation Satellite System (GNSS) technologies face limitations due to satellite orbit, ionospheric errors, and signal quality.
  • Distributed cooperative positioning offers potential for improved accuracy but is hindered by measured range and node positioning errors.

Purpose of the Study:

  • To propose a novel factor graph-aided distributed cooperative positioning algorithm.
  • To address the limitations of current cooperative positioning methods in mitigating ranging and positioning errors.
  • To enhance the positioning accuracy of cooperative nodes within GNSS systems.

Main Methods:

  • Development of a factor graph-aided distributed cooperative positioning algorithm.
  • Establishment of a confidence function using factor graph theory to model ranging and positioning errors.
  • Fusion of positioning information from cooperative nodes via the confidence function.

Main Results:

  • The proposed algorithm demonstrates a 30-60% improvement in positioning accuracy compared to other algorithms under identical error conditions.
  • The algorithm significantly reduces convergence time, achieving rates 1/5 to 1/3 of comparative methods.
  • The factor graph approach effectively mitigates the impact of measured range and positioning errors.

Conclusions:

  • The factor graph-aided distributed cooperative positioning algorithm enhances navigation and positioning accuracy for GNSS systems.
  • This method is crucial for advancing smart city infrastructure by overcoming current positioning limitations.
  • The algorithm offers superior performance in terms of accuracy, convergence speed, and error reduction.