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Machine Learning (ML) and Artificial intelligence (AI) have increased automation potential within defense applications such as border protection, compound security, and surveillance applications. Advances in low-size weight and power (SWAP) computing platforms and unmanned aerial systems (UAS) have enabled autonomous systems to meet the critical needs of future defense systems. Recent academic advances in deep learning aided computer vision yielding impressive results on object detection and recognition, necessary capabilities to enable autonomy in defense applications. These advances, often open-sourced, enable the opportunistic integration of state-of-the-art (SOTA) algorithms. However, these systems require a large amount of object-relevant data to transfer from general academic domains to more relevant situations. Additionally, UAS systems require costly verification and validation of autonomy logic. These challenges can lead to high costs for both training data generation and costly field autonomy integration and testing activities. To address these challenges, in conjunction with partners, Elbit America has developed a multipurpose synthetic simulation environment capable of generating synthetic training data and prototyping, verifying, and validating autonomous distributed behaviors. We integrated a thermal modeling capability into Unreal Engine to create realistic training data by enabling the real-time simulation of SWIR, MWIR, and LWIR sensors. This radiometrically correct sensor model capability enables the simulation-based training data generation for our object recognition and classification pipeline, called Rapid Algorithm Development and Deployment (RADD). Several drones were instantiated using emulated flight controllers to enable end-to-end autonomy training and development before hardware availability. Herein, we describe an overview of the simulation environment and its relevance to detection, classification, and distributed autonomous decision-making.
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