Reservoir Computing for Drone Trajectory Intent Prediction: A Physics Informed Approach.

Area of Science:

• Control Engineering
• Machine Learning
• Aerospace Engineering

Background:

• Accurate drone trajectory prediction is essential for effective countermeasures against anomalous drone behavior.
• Inaccurate predictions can lead to high false-positive rates, compromising critical infrastructure safety.

Purpose of the Study:

• To propose a novel physics-informed reservoir computing (PIRC) scheme for enhanced drone trajectory prediction.
• To improve the accuracy and generalization capabilities of drone trajectory prediction algorithms.

Main Methods:

• A hybrid approach combining a standard reservoir computing scheme for high-dimensional data with a nonlinear control scheme.
• The nonlinear control scheme utilizes prediction error dynamics and feedback linearization for weight optimization.
• Lyapunov stability theory is employed to guarantee algorithm convergence and boundedness.

Main Results:

• Two distinct PIRC schemes were developed, demonstrating enhanced prediction robustness.
• The proposed methods effectively minimize prediction errors through physical feedback integration.
• Simulation studies validated the superior performance and reliability of the PIRC approach.

Conclusions:

• The developed PIRC schemes offer a robust solution for accurate drone trajectory prediction.
• This approach enhances safety by reducing false positives in drone detection and countermeasure systems.
• The integration of physics principles into reservoir computing significantly improves predictive model performance.