To collect, transmit and archive data from armament systems (artillery, mortars, crew served, remote, squad) for use in AI/ML applications.
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Armament System AI Data Logger & Architecture
Artificial Intelligence/Machine Learning
supply chain management, logistics coordination, target identifications and simulation
Synthetic RF Training Data Generation
The purpose of this topic is to establish a comprehensive RF-based database that will be used to train a deep learning computer vision algorithm for a target detection system.
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Natural Language Processing
The objective of this topic is to develop Artificial Intelligence / Machine Learning models to augment Natural Language Processing (NLP) capabilities in 2 main challenge areas: relationship detection and aggregation – automatically detecting relationships that exist between the entities that were extracted from the data. Some extracting attributes of entities include: hair color, nationality, model of tank, armor of tank. Pattern recognition, analysis, and exploitation – automatically recognizing patterns such as indications and warnings and courses of actions and analyzing them – is an integral part of this topic.
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Machine Learning (ML) for Breach Routing
This program aims to improve the Explosive Breacher (EB) by closing capability gaps in current breaching techniques left by quickly evolving adversarial technology and aging legacy equipment.
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Graph Neural Networks (GNN) for UxS Collaborative Agent Control
The purpose of this topic is to use recent advancements in Artificial Intelligence, specifically, GNN to be able to collaborate between different AI agents, Unmanned Aerial/Ground Systems (UxS).
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Height of Burst Scoring through Machine Learning
The purpose of this topic is to develop an AI/ML approach to score height of burst automatically, eliminating the man-power required by the current method.
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Asynchronous Neuromorphic Digital Readout Circuit for Infrared Cameras for Autonomous Target Acquisition and Autonomous Vehicles
Most military scenarios consist of highly cluttered and dynamic scenes. Asynchronous on chip smart event cameras can eliminate cluttered scenarios with a much-reduced latency, power, and would be able to hand off images of interest to imbedded autonomous target algorithms.
Artificial Intelligence/Machine Learning (AI/ML) for Radio Frequency (RF) Modulation Recognition
The purpose of this topic is to demonstrate the ability to interface to a modern Software Defined Radio (SDR) and the Photon digital signal processing framework in order to characterize large swaths of the RF spectrum in near-real-time (NRT) using AI/ML techniques for signal modulation recognition and sorting (Blue Force emitters; Red Force emitters; Civilian emitters);
Sensor Synthetic Data Generation
US Army requires large-scale, accurate and easily accessible training, test, and validation data to support AI model development for multiple security domains (e.g. SIPR, JWICS…).
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Datalink-Enabled AI for Fires Optimization
It is projected in the future fight, the speed of battle will be imperative and every munition employed must be effective.
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