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Improved GNSS Localization and Byzantine Detection in UAV Swarms.

Shlomi Hacohen^1,2, Oded Medina¹, Tal Grinshpoun^2,3

¹Department of Mechanical Engineering, Ariel University, Ariel 4070000, Israel.

Sensors (Basel, Switzerland)

|December 22, 2020

Summary

This study introduces a new method for unmanned aerial vehicle swarm localization that detects and mitigates errors from internal failures or external jamming. It ensures reliable localization even with disruptions.

Keywords:

Byzantine detection FANET GNSS localization UAV swarm pool testing

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Area of Science:

Robotics and Control Systems
Distributed Systems
Sensor Networks

Background:

Unmanned aerial vehicle (UAV) swarms require accurate localization for many tasks.
Localization systems are susceptible to measurement errors.
These errors are exacerbated by internal Byzantine failures or external disruptions like GNSS jamming.

Purpose of the Study:

To develop an improved distance-based localization method for UAV swarms.
To create schemes for detecting endogenous (Byzantine agents) and exogenous (external source) disruptions.
To reduce communication and computation overhead using pool testing.

Main Methods:

Improved distance observation for localization.
Byzantine agent detection schemes for internal failures.
Disrupted area detection for external interference.
Pool testing to optimize communication and computation.

Main Results:

Demonstrated effectiveness of proposed methods through simulations.
Reliable error estimation achieved even under disruption conditions.
Successful integration of disruption identification into the localization process.

Conclusions:

The developed methods enhance the reliability of UAV swarm localization.
The approach effectively identifies both internal and external sources of disruption.
Pool testing optimizes the efficiency of disruption detection schemes.