V3I7P49

Intelligent Adaptive Guidance of UAVs Using AI-Based Trajectory Prediction for Intercepting Highly Maneuvering Fighter Aircraft 

Dao Trung Hieu*¹

Abstract

The interception of highly maneuvering fighter aircraft by unmanned aerial vehicles (UAVs) remains a significant challenge due to rapid target maneuvers, nonlinear dynamics, and uncertainties in the engagement environment. Conventional guidance laws primarily rely on the target’s current state, which often leads to degraded interception performance when facing abrupt maneuvering targets. This paper proposes an intelligent adaptive guidance framework that integrates artificial intelligence (AI)-based trajectory prediction with a real-time adaptive guidance law for autonomous UAV interception. The proposed framework first estimates the target state using a nonlinear state estimation algorithm and then employs a deep learning-based trajectory prediction model to forecast the future motion of the fighter aircraft over a finite prediction horizon. The predicted trajectory is subsequently used to generate an optimal interception point, enabling the guidance law to proactively adjust the UAV flight path rather than reacting solely to instantaneous target motion. Furthermore, an adaptive guidance strategy is developed to compensate for prediction uncertainty and continuously update guidance commands during the engagement. Numerical simulations involving highly maneuvering fighter targets under multiple combat scenarios demonstrate that the proposed method significantly improves trajectory tracking accuracy, reduces miss distance, shortens interception time, and decreases control effort compared with conventional proportional navigation and adaptive proportional navigation methods. The results indicate that integrating AI-based trajectory prediction with adaptive guidance provides an effective and robust solution for intelligent autonomous UAV interception in complex air combat environments.

Keywords:

UAV interception, adaptive guidance, trajectory prediction, artificial intelligence, deep learning, fighter aircraft, autonomous guidance, intelligent control.