Modern tactical missile guidance laws require an accurate target tracking and state estimation to effectively intercept maneuvering targets. The Proportional Navigation (PN) is one of the most widely used guidance laws for tactical missiles, relying on line-of-sight (LOS) rate measurements to generate lateral acceleration commands. Missiles typically employ electro-optical or infrared seekers. Our study utilizes a two-axis gimbaled camera seeker which employs a laser range finder, equipped with the YOLO algorithm for a real-time target detection tracking and recognition. The seeker provides the image acquisition, target detection, tracking, and LOS rate measurement relative to the inertial frame. Additionally, an Extended Kalman Filter (EKF) is used to filter the LOS rate measurements and to estimate the target three-dimensional motion, as well as lateral accelerations. The estimates are then used to implement the Augmented Proportional Navigation (APN) which reduces the required missile lateral acceleration compared to the standard PN guidance. The study findings are validated using a six-degrees-of-freedom (6-DOF) nonlinear missile model, demonstrating its effectiveness when integrating the deep-learning-based target tracking with the modern guidance laws.
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