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A Multi-Sensor Tight Fusion Method Designed for Vehicle Navigation.

Qifeng Lai1,2, Hong Yuan1, Dongyan Wei1

  • 1Aerospace Information Research Institute, Chinese Academy of Science, Beijing 100864, China.

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|May 6, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a multi-sensor tight fusion (MTF) method for improved vehicle navigation. By integrating Global Navigation Satellite System (GNSS) with other sensors, it enhances positioning reliability in challenging urban environments.

Keywords:
GNSSINSbarometric altimeterclock modelodometertight fusion

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Area of Science:

  • Navigation Systems
  • Sensor Fusion
  • Robotics

Background:

  • Global Navigation Satellite System (GNSS) signals are vulnerable in urban environments, compromising vehicle navigation reliability.
  • Advancements in sensor technology offer potential for improving GNSS positioning through multi-sensor fusion.

Purpose of the Study:

  • To enhance the continuity and reliability of vehicle navigation systems.
  • To propose and evaluate a multi-sensor tight fusion (MTF) method integrating GNSS with other sensors.

Main Methods:

  • Developed a multi-sensor tight fusion (MTF) method.
  • Combined Global Navigation Satellite System (GNSS), inertial navigation system (INS), odometer, and barometric altimeter.
  • Tested different fusion strategies in open-sky, insufficient satellite, and satellite outage scenarios.

Main Results:

  • In open-sky conditions, sensor errors were accurately estimated for noise compensation and accuracy improvement.
  • With limited satellite availability, the MTF method achieved accuracy close to open-sky performance using a barometric altimeter and clock model.
  • During satellite outages, MTF exhibited slower error divergence compared to traditional GNSS/INS integration, aided by odometer and barometric altimeter.

Conclusions:

  • The proposed MTF method significantly improves vehicle navigation continuity and reliability, especially in GNSS-degraded environments.
  • Sensor fusion effectively compensates for GNSS signal vulnerabilities, offering robust positioning solutions.
  • The integration of INS, odometer, and barometric altimeter provides a resilient navigation system for complex scenarios.