supply chain management, logistics coordination, target identifications and simulation
The objective of this topic is to apply Large Language Models (LLMs) and/or other Artificial Intelligence (AI) approaches to support and automate warfighter’s system’s integrations. This will pertain to problems with data unification and interoperability regardless of the target system, source system, or data format. It will focus on usage in tactical environments to assist and provide reliable performance, regardless of echelon level.
Artificial Intelligence for Interoperability Read More »
Operating, maneuvering, engaging, defending, and commanding the Combat Vehicles on the modern battlefield requires a significant level of crew communication, systems management, and situational awareness.
AI/ML-Enabled Voice-Commanded Autonomous Maneuver for Ground Combat Vehicles Read More »
Automated Course of Action (CoA) recommendation at the Unit of Action. Currently, it takes units at the Battalion (BN) echelon several hours to use the Military Decision-Making Process (MDMP) to generate and vet CoA options.
Leveraging state-of-the-art Artificial Intelligence/Machine Learning (AI/ML) algorithms will speed up this process by an order of magnitude, allowing systematic replanning during the execution phase of operations. This will improve mission success and reduce risk to force in combat operations. It will also enable the mobile, distributed command post concept.
Automated Course of Action Generation Read More »
The proposed topic will develop a Large Language Model (LLM) tailored to reduce the risk of traditional and emerging Artificial Intelligence (AI) and Machine Learning (ML) adversarial attacks on aviation and missile systems.
Large Language Models for System Security Engineering Analysis Read More »
The Explosive Ordnance Disposal Visual Ordnance Identification Database (EODVOID) will develop an automated photogrammetry method to greatly increase the speed of scanning and creating 3D models for 1000’s of pieces of ordnance samples. This would enable the development of a much-needed authoritative ordnance database and serve as a baseline standard for training and developing AI/ML detection and classification algorithms.
Explosive Ordnance Disposal Visual Ordnance Identification Database (EODVOID) Read More »
The objective of this SBIR topic is to advance methods for generating and labeling synthetic data representing various classes of Radio Frequency (RF) signals. This synthetic data will support the training of Electronic Support and Signals Intelligence (SIGINT) models aimed at enhancing automated detection, characterization, and identification (DCI) of Signals of Interest (SoI).
The objective of this research topic is to explore Boyd’s Observe, Orient, Decide and Act Loop with the goal of finding disruptive courses of action in a multi-domain environment that allow warfighters to impact both the rate of engagement with a competitor, but also the rhythm of engagement that allow our commanders and warfighters to leverage both the complexity and dynamism inherent in a multi-domain operation to create decisive wins through strategic surprise.
Large Language Model Course of Action Analysis Read More »
The proposed SBIR topic aims to advance the capabilities of large language models by addressing critical challenges and enhancing functionalities relevant to military applications, particularly within the U.S. Army.
Dynamic Generative Large Language Model for Continuous Situational Awareness Read More »
To equip artisans with technology that enhances operational capabilities, their breadth of logistical support, and property accountability. Proposals should allow for the flexibility of artisans to respond to mixed model production and innovations that enable artisans to execute ergonomically challenging tasks. There is a need for technologies that equip artisans with standard yet flexible enduring technologies to sustain air missile defense and power generation systems, ensuring that assets are returned to combat swiftly.
Shop Tools and Enablers Open Topic Read More »
This open topic accepts both Phase I and Direct to Phase II submissions. Phase I proposals are accepted for a cost up to $250,000 for a 6-month period of performance and Direct to Phase II proposals are accepted for a cost up to $2,000,000 for a 24-month period of performance. All submissions most address the following 6 AI sub-fields: Synthetic data generation in a format applicable to a given situation that is not obtained by direct measurement. This includes visual, textual, video, geospatial, and sensor data.
Artificial Intelligence/ Machine Learning (AI/ML) Focused Open Topic Read More »
