In recent years, loitering munitions— particularly unmanned aerial vehicles (UAVs)—have become pivotal in contemporary warfare due to their versatility and precision. The crucial challenge related to UAV design is the mid-course guidance algorithm that precisely ensures the optimum tracking problem of the desired path. Moreover, during the terminal phase, there is a crucial need for a suitable terminal guidance law to improve interception accuracy against both stationary and maneuvering targets. This study proposes and compares three geometrically-based mid-course guidance algorithms —Carrot Chasing (CC), Pure Pursuit Line-of-Sight (PLOS), and Nonlinear Guidance Law (NLGL)— using high-fidelity simulations of a nonlinear fixed-wing UAV model. A performance-based comparison is used to determine the most efficient strategy for integration with the UAV’s flight-control system. Additionally, the terminal phase incorporates the Augmented Proportional Navigation (APN) method, an optimal control-based strategy, to achieve higher interception precision with reduced control demand. The APN’s performance is investigated in comparison with the classical Proportional Navigation (PN) law. Simulation outcomes affirm the superiority of the NLGL during mid-course navigation which decreases the Cross-Track Error by 33.3% compared to the other methods and increases the speed by 30% at least, complemented by the APN's ability to minimize miss distance in case of target maneuver by 37% and lower the vehicle’s acceleration demands.
Kapeel et al. (Mon,) studied this question.