Vision-Based Missile Guidance System Using YOLOv8 and DeepSORT for Object Tracking With Proporational Navigation Simulation in MATLAB

This Reaseach presents Vision-based Missile Guidance System integrating machine learning techniques for object detection and tracking. The system integrates the device with DeepSORT algorithm for robust tracking. The integration of computer vision and artificial intelligence into missile guidance systems leads to three essential benefits: better precision, quick response functionality, as well as minimum usage of conventional inertial sensors. The efficient YOLOv8 algorithm performs quick target identification through object detection then DeepSORT completes reliable multi-object tracking processes. The research presents a complete method for enhancing missile targeting precision through the integration of PN navigation with both techniques. The target engagement situations undergo analysis through simulated MATLAB environments that track stationary and moving targets. Performance evaluation consists of trajectory optimization measures that evaluate both miss distance performance and tracking reliability standards