Missile Guidance using Proportional Navigation and Machine Learning

Abstract

: 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.