AI-Based Predictive Laser Beam Control for UAV Interception in Directed Energy Weapon Systems Using Velocity-Based Trajectory Prediction

Abstract

The rapid proliferation of unmanned aerial vehicles (UAVs) in defence and surveillance zones presents an escalating challenge to national security, demanding the deployment of agile, real-time interception systems. While high-energy laser-based directed energy weapons (DEWs) provide a non-kinetic alternative to missile defence, their performance is hampered by actuator latency, beam jitter, and inability to track erratically manoeuvring drones. To overcome these challenges, this study proposes a novel AI-augmented predictive beam control framework that anticipates UAV trajectories by estimating future positions using velocity-informed regression modelling. The system, developed in MATLAB, simulates and predicts evasive UAV motions sinusoidal, zigzag, and spiral through a shallow neural network trained on synthetic trajectory data. It incorporates beam delay modelling and sinusoidal jitter to replicate real-world laser transmission imperfections. The predictive model achieves a tracking accuracy of 94.12% and a mean squared error (MSE) of 0.1834, validated across diverse UAV paths. Key simulation outcomes include six comparative trajectory plots, error histograms, and cumulative accuracy profiles that highlight the system’s robustness. This research contributes (i) an integrated beam latency compensation module for predictive DEWs, (ii) a simulation dataset curated from recent UAV motion and targeting literature, and (iii) a control framework extendable to reinforcement learning (RL) and Kalman filtering approaches for adaptive targeting. The proposed framework advances the design of autonomous DEW systems by enhancing hit accuracy, energy efficiency, and responsiveness in contested aerial environments. The framework’s modular design enables extensions to real-world testing and broader applications in high-speed target tracking beyond defence systems.