Mr. Joshua Haley Profile

Joshua Haley

at Elbit Systems of America, LLC

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Publications (3)

Proceedings Article | 14 June 2023 Paper

Multi-agent collaboration environment simulation

Joshua Haley, Jonathan Tucker, Jonathan Nesper, Brian Daniel, Trisha Fish

Proceedings Volume 12529, 125290P (2023) https://doi.org/10.1117/12.2663828

KEYWORDS: Data modeling, Object detection, Device simulation, Sensors, Infrared sensors, Electro optical modeling, Computer simulations, Environmental sensing

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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.

Proceedings Article | 12 September 2021 Presentation + Paper

Bridging the gap from academia to application: rapid algorithm design and deployment for artificial intelligence (RADD-AI)

Joshua Haley, Jonathan Tucker, Brandon Kessler, Trisha Fish

Proceedings Volume 11870, 118700A (2021) https://doi.org/10.1117/12.2601858

KEYWORDS: Data modeling, Algorithm development, Detection and tracking algorithms, Sensors, Surveillance, Artificial intelligence, Systems modeling, Performance modeling

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Machine Learning (ML) and Artificial intelligence (AI) have led to an increase in automation potential within defense applications such as border protection, compound security, and surveillance applications. Recent academic advances in deep learning aided computer vision have yielded impressive results on object detection, and recognition, necessary capabilities to increase automation in defense applications. These advances are often open-sourced, enabling the opportunistic integration of state-of-the-art (SOTA) algorithms into real systems. However, these academic achievements do not translate easily to engineered systems. Academics often are looking at a single capability with metrics such as accuracy or F1 score without consideration of system-level performance and how these algorithms must integrate or what level of computational performance is required. An engineered system is developed as a system of algorithms that must work in conjunction with each other with deployment constraints. This paper describes a system, called Rapid Algorithm Design and Deployment for Artificial Intelligence (RADD-AITM), developed to enable the rapid development of systems of algorithms incorporating these advances in a modular fashion using networked Application Programming Interfaces (APIs). The inherent modularity mitigates the assumption of monolithic integration within a single ecosystem that creates vendor lock. This monolith assumption does not account for the reality that frameworks are usually targeted toward different types of problems and learning vs inference capabilities. RADD-AI makes no such assumption. If a different framework solves subsets of the system more eloquently, they can be integrated into the larger pipeline. RADD-AI enables the integration of state-of-the-art ML into deployed systems while also supporting the necessary ML engineering tasks, such as transfer learning, to operationalize academic achievements. To motivate how RADD-AI enables applications of ML/AI, we detail how this system is used to implement a defense application, a border surveillance capability, via the integration of detection, recognition, and tracking algorithms. This system, implemented and developed within RADD-AI, utilizes several SOTA models and traditional algorithms within multiple frameworks bridging the gap from academic achievement to fielded system.

Proceedings Article | 21 April 2020 Paper

Cluster analysis of deep embeddings in real-time strategy games

Joshua Haley, Adam Wearne, Cameron Copland, Eric Ortiz, Amanda Bond, Mike van Lent, Rob Smith

Proceedings Volume 11413, 114131I (2020) https://doi.org/10.1117/12.2558105

KEYWORDS: Machine learning, Visualization, New and emerging technologies

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