SiDG-ATRID: Simulator for Data Generation for Automatic Target Recognition, Identification and Detection
Primary Investigator:
Shreyas Sundaram
Younggil Chang, Alec Andrulis, Isabel Hoppe
Abstract
The increased utilization of Unmanned Aerial Vehicles (UAVs) in diverse missions, from humanitarian aid to combat operations, underscores the necessity for an efficient and cost-effective development workflow for autonomous systems. Especially for defense purposes, building autonomous target recognition systems capable of detecting, identifying, and classifying adversarial agents with machine learning models requires extensive data for training. Consequently, simulation software has become an essential tool for developers seeking to assess autonomous system performance and collect data across various environments. Furthermore, the transition to real-world, application-ready systems necessitates a simulation platform that replicates not only the vehicle control algorithms but also environmental factors that affect system performance, such as lighting conditions and sensor noise.
In response to these requirements, we introduce ‘SiDG-ATRID’ (Simulator for Data Generation for Automatic Target Recognition, Identification and Detection), a simulation platform that enables the collection of high-fidelity imagery data, powered by Unreal Engine 5. The simulator supports multi-agent simulations using the AirSim API library for UAV controls and simulates commercial aircraft traffic. This framework allows for customized camera placements to record videos or photos and manage environmental conditions such as weather and lighting. Additionally, by leveraging the Cesium API for geospatial mapping, it can accurately recreate real-world environments, enhancing the realism and applicability of simulations. This integrated approach enhances the efficiency and effectiveness of synthetic data generation for detection tasks, enabling developers to easily configure simulations and collect diverse data.