Improved GNSS Localization and Byzantine Detection in UAV Swarms.

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.