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Improved In-Flight Estimation of Inertial Biases through CDGNSS/Vision Based Cooperative Navigation.

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Cooperative navigation using multiple unmanned aerial vehicles (UAVs) enhances in-flight estimation of inertial sensor biases. Utilizing differential Global Navigation Satellite System (GNSS) and visual tracking improves attitude accuracy for better bias determination.

Keywords:
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Area of Science:

  • Aerospace Engineering
  • Robotics
  • Navigation Systems

Background:

  • Accurate estimation of inertial sensor biases is critical for Unmanned Aerial Vehicle (UAV) navigation.
  • Traditional methods can be limited by sensor drift and environmental factors.

Purpose of the Study:

  • To develop and evaluate a cooperative navigation strategy for improving in-flight estimation of inertial sensor biases on UAVs.
  • To leverage multi-vehicle interactions for enhanced navigation accuracy.

Main Methods:

  • A cooperative navigation strategy involving a "chief" UAV and one or more "deputy" UAVs.
  • Utilizing carrier-phase differential Global Navigation Satellite System (GNSS) for precise positioning.
  • Employing visual tracking of deputy aircraft by the chief UAV.
  • Integrating GNSS and visual data within a 15-state extended Kalman filter.

Main Results:

  • Cooperative navigation with two deputy UAVs significantly improves inertial bias estimation.
  • A single deputy UAV can be effective if relative geometry and dynamics changes are considered.
  • Experimental validation using multi-rotor UAVs in formation demonstrated the framework's efficacy.

Conclusions:

  • The proposed cooperative navigation framework effectively enhances in-flight inertial sensor bias estimation for UAVs.
  • The strategy is adaptable and applicable to various UAV platforms beyond small multi-rotors.