Unmanned aerial vehicles (UAVs) are increasingly critical for long-endurance surveillance and border-patrol missions, offering persistent monitoring, rapid situational awareness, and reduced risk to human operators. However, achieving reliable loitering and accurate target tracking in the presence of dynamic targets, variable winds, and nonlinear flight dynamics remains challenging when using conventional guidance strategies and classical control methods, often resulting in tracking errors, frequent course corrections, and increased energy consumption. This paper addresses these challenges through a unified architectural integration of guidance, estimation, and control components within a cohesive framework. The proposed architecture combines a novel adaptive loiter radius algorithm, a predictive motion estimator, and a gain-scheduled PID autopilot to operate synergistically rather than as isolated modules. The adaptive loiter radius algorithm dynamically adjusts the loiter radius (150–800 m), pattern center, and flight trajectory based on real-time target motion, achieving a 73% reduction in lateral path deviation compared to conventional Line-of-Sight (LOS) guidance. In parallel, the predictive motion estimator anticipates target movements during both straight and turning flight segments, reducing target miss distance by 68%. A hybrid mode-switching logic is incorporated within the architecture to ensure seamless transitions between wide-area surveillance and focused target tracking. At the control level, the gain-scheduled PID controller preserves aerodynamic stability across varying flight conditions, including changes in speed, angles of attack, and wind disturbances (±8 m/s). The complete integrated architecture is modeled and validated in MATLAB/Simulink using nonlinear UAV dynamics, sensor imperfections, and environmental disturbances. Monte Carlo analyses (N = 500) demonstrate an approximate 42% reduction in energy consumption. The results confirm that the proposed architecture-driven integration of adaptive guidance, predictive estimation, and robust control yields significant improvements in tracking accuracy, robustness, and mission efficiency, highlighting the effectiveness of system-level architectural design in enhancing autonomous UAV surveillance and border-patrol operations.
SHAHEEN et al. (Tue,) studied this question.