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UAV Swarm Navigation Using Dynamic Adaptive Kalman Filter and Network Navigation.

Jingjuan Zhang1, Wenxiang Zhou1, Xueyun Wang1

  • 1School of Instrument Science and Optoelectronics Engineering, Beihang University, XueYuan Road No. 37, HaiDian District, Beijing 100191, China.

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

This study enhances unmanned aerial vehicle (UAV) swarm positioning accuracy. A dynamic adaptive Kalman filter (DAKF) improves individual UAV navigation, while a network navigation algorithm (NNA) ensures swarm accuracy during Global Navigation Satellite System (GNSS) outages.

Keywords:
adaptive Kalman filterensemble empirical mode decompositionnetworked navigationobservation noiseprocess noise

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Area of Science:

  • Robotics and Control Systems
  • Navigation and Positioning Technologies
  • Aerospace Engineering

Background:

  • Accurate positioning is critical for unmanned aerial vehicle (UAV) swarm operations, especially under varying Global Navigation Satellite System (GNSS) availability.
  • Traditional Kalman Filter (KF) based inertial navigation system (INS)/GNSS-integrated systems are sensitive to inaccurate noise models, potentially leading to navigation degradation and divergence.
  • GNSS outages pose a significant challenge for maintaining UAV swarm positioning accuracy.

Purpose of the Study:

  • To propose and validate a robust two-case navigation scheme for improving UAV swarm positioning accuracy.
  • To enhance individual UAV navigation accuracy using a dynamic adaptive Kalman filter (DAKF) that adaptively adjusts noise parameters.
  • To ensure swarm positioning accuracy during GNSS outages using a network navigation algorithm (NNA).

Main Methods:

  • Developed a dynamic adaptive Kalman filter (DAKF) that utilizes ensemble empirical mode decomposition (EEMD) to determine the observation noise covariance matrix (R) and adaptively adjust the process noise covariance matrix (Q).
  • Implemented a network navigation algorithm (NNA) that leverages inter-UAV distance information to compensate for inertial navigation system (INS) position errors during GNSS outages.
  • Conducted simulations to evaluate the performance of the proposed DAKF and NNA methods under different navigation scenarios.

Main Results:

  • The DAKF significantly improved the positioning accuracy of individual UAVs by 30-50% compared to standard Kalman filtering.
  • The NNA demonstrated a substantial increase in swarm positioning accuracy, achieving a 93% improvement during simulated GNSS outages.
  • The proposed two-case navigation scheme effectively addresses positioning challenges in both GNSS-available and GNSS-denied environments.

Conclusions:

  • The DAKF provides a more robust and accurate navigation solution for individual UAVs by mitigating issues related to inaccurate noise models.
  • The NNA effectively maintains UAV swarm coordination and positioning accuracy even when GNSS signals are unavailable, utilizing inter-agent communication.
  • The integrated navigation scheme offers a comprehensive solution for reliable UAV swarm operations across diverse environmental conditions.