An optimization of target classification tracking and mathematical modelling for control of autopilot

Abstract

ABSTRACT This paper describes the optimization of target categorization tracking and mathematical modelling to control the autopilot in the missile guidance system, which can recognize any type of target using intelligent devices and image processing. A typical inaccuracy in a reliable connection to the flight path can be reduced by using imaging techniques and new cell communication-based fruit fly swarm optimization within the object recognition processes. The suggested approach is able to locate a target among a batch of visual images captured from a camera, despite the inclusion of two targeting artefacts and their nearly similar layout. Furthermore, it has been proposed to not only evaluate movement objects but also track artefacts with consistent rotation speeds and steady speeds. Aside from adjusting the yaw, pitch, and roll axes, a simulation result for auto-pilot regulation has now been used to control a flight path. Using MATLAB and Simulink software, a simulated missile model was created that moves at 328 m/s toward the target and hits it in 3.25 seconds.