Missile Guidance using Proportional Navigation and Machine Learning
by Mirza Hodžić 
and Naser Prljača *
Control Systems and Robotics, Faculty of Electrical Engineering, University of Tuzla, Tuzla, 75000, Bosnia and Herzegovina
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 3, Issue 3, Page # 19-26, 2024; DOI: 10.55708/js0303003
Keywords: Missile Guidance, YOLO, Missile Seeker, Proportional Navigation, 6DOF
Received: 23 January 2024, Revised: 29 February 2024, Accepted: 11 March 2024, Published Online: 19 March 2024
APA Style
Hodžić, M., & Prljača, N. (2024). Missile Guidance using Proportional Navigation and Machine Learning. Journal of Engineering Research and Sciences, 3(3), 19-26. [DOI: 10.55708/js0303003]
Chicago/Turabian Style
Hodžić, Mirza, and Naser Prljača. 2024. “Missile Guidance using Proportional Navigation and Machine Learning.” Journal of Engineering Research and Sciences 3, no. 3 (2024): 19-26. DOI: 10.55708/js0303003.
IEEE Style
Variants of proportional navigation (PN) are perhaps mostly used guidance laws for tactical homing missiles. PN aims to generate commanding missile lateral acceleration proportional to line of sight (LOS) angular rate, so that missile velocity vector rotates in such a way to assure interception of a target. In order to generate commanding lateral accelerations, the guidance system needs measurements of LOS angular rate and the closing velocity between the missile and the target, or the missile velocity. A device which provides guidance information is referred to as the missile seeker. In the case of imaging based seekers (visible light (EO), infrared light (IIR)), LOS rate is estimated using imaging sensor, while closing or missile velocity is measured using appropriate sensors or guess estimated. In this paper, we present the design and simulation of a missile homing system which includes: true PN guidance law, linear multiloop acceleration autopilot, and gimbaled imaging based missile seeker. Target seeker uses advanced deep machine learning object detection YOLO (You only look once) model, for target detection and tracking as well as LOS rate estimation. Comprehensive simulation model, consisting of full 6DOF missile and controls dynamics, 3D world and camera model, is developed. Intensive simulation results show performances of the proposed missile homing system.
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