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Resilient Multi-Sensor UAV Navigation with a Hybrid Federated Fusion Architecture.

Sorin Andrei Negru1, Patrick Geragersian1, Ivan Petrunin1

  • 1School of Aerospace, Transport, and Manufacturing, Cranfield University, Cranfield MK43 0AL, UK.

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|February 10, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new hybrid fusion architecture for unmanned aerial vehicle (UAV) navigation in cities. It improves position, navigation, and timing (PNT) accuracy and resilience, even with GPS signal loss or urban interference.

Keywords:
EKFGRUUAVcomputer visionhybrid fusionmultipathresilient navigationurban air mobility

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Area of Science:

  • Robotics and Autonomous Systems
  • Navigation and Control Systems
  • Sensor Fusion

Background:

  • Urban UAV operations require robust Position, Navigation, and Timing (PNT) solutions.
  • Global Navigation Satellite System (GNSS) accuracy degrades in urban areas due to non-line-of-sight (NLOS) and multipath effects.
  • Existing fusion architectures struggle to maintain PNT accuracy under urban signal interference.

Purpose of the Study:

  • To develop and evaluate a novel hybrid federated fusion architecture for resilient UAV navigation in urban environments.
  • To address the challenges of GNSS multipath and performance degradation in complex urban settings.
  • To ensure the continuity and accuracy of PNT data for UAVs operating in challenging conditions.

Main Methods:

  • A hybrid federated fusion architecture integrating data from GNSS, Inertial Measurement Unit (IMU), monocular camera, and barometer.
  • Implementation of Extended Kalman Filters (EKFs) for local filters and a Gated Recurrent Unit (GRU) block as the master filter.
  • Data collection using AirSim for virtual environments and Spirent GSS7000 hardware for real-world sensor data.

Main Results:

  • The proposed hybrid fusion architecture demonstrated improved resilience and robustness compared to a classic federated architecture.
  • The system maintained good positioning performance with a 95th percentile error of 0.54 m (square scenario) and 1.72 m (survey scenario) under degraded conditions.
  • The architecture effectively mitigated GNSS multipath and performance degradation, including during GNSS outages.

Conclusions:

  • The novel hybrid federated fusion architecture offers a robust and resilient PNT solution for UAVs in urban environments.
  • The integration of multiple sensors and advanced filtering techniques (EKF, GRU) enhances navigation accuracy and reliability.
  • This approach provides a pathway for dependable UAV operations in complex, signal-challenged urban landscapes.