MILITARY COMMON OPERATING PICTURE

Abstract

This thesis introduces an Artificial Intelligence (AI)-powered Military Common Operating Picture (MCOP) specifically designed to address the limitations faced by the Pakistani Army's Armored Corps operating in the field environments with limited visibility. It focuses on enhancing situational awareness for the commanders and the soldiers within armored vehicles by integrating several key functionalities. An Internet of Things (IoT)-based Blue Force Tracking system leverages real-time sensor data to pinpoint the location and orientation of friendly forces. Furthermore, a YOLOv8 deep learning model trained on a comprehensive dataset facilitates automatic enemy assets detection and classification. Finally, the existing Battlefield Management System (BMS) is optimized through the integration of enhanced geospatial databases featuring various map layers and the implementation of standardized military symbology. This unified AI-powered MCOP empowers commanders with a comprehensive and real-time view of the battlefield, fostering improved decision-making, faster response times, and ultimately, a decisive edge in mission accomplishment.