Robust unmanned aerial vehicles tracking amid electronic interference utilizing auxiliary particle filtering

Area of Science:

• Robotics and Control Systems
• Signal Processing
• Navigation and Guidance

Background:

• Electronic interference significantly degrades Unmanned Aerial Vehicle (UAV) tracking accuracy and safety.
• Existing tracking filters like EKF and UKF struggle with interference-induced measurement errors.
• Critical applications like surveillance and disaster response demand robust UAV navigation.

Purpose of the Study:

• To introduce and evaluate a novel Auxiliary Particle Filter (APF) for robust UAV tracking under electronic interference.
• To demonstrate the APF's effectiveness in mitigating measurement errors caused by interference.
• To provide implementation guidelines for practical adoption in fixed-reference scenarios.

Main Methods:

• Development and application of an Auxiliary Particle Filter (APF) with adaptive proposal distributions.
• Implementation of robust weight updates to counteract interference effects.
• Comprehensive simulations evaluating performance under various interference levels and sensor degradation.

Main Results:

• The APF achieved a mean Root Mean Square Error (RMSE) of 4.82 meters, outperforming EKF and UKF.
• The APF maintained stable performance even under severe interference, unlike conventional filters.
• Statistical validation confirmed significant improvements across all tested scenarios (p < 0.001).

Conclusions:

• The Auxiliary Particle Filter (APF) offers a robust and reliable solution for UAV tracking in interference-prone environments.
• The APF enhances navigation accuracy and operational safety for critical UAV applications.
• This study paves the way for adapting the APF to more dynamic autonomous UAV tracking scenarios.