Artificial Intelligence/Machine Learning

The Army demilitarizes non-usable ammunition in the rotary kiln incinerator (RKI) and tries to recycle the scrap through commercial dealers.

Develop and demonstrate Machine Learning based radar algorithms/techniques to improve Point of Origin (POO) Target Location Accuracy (TLA) in Counter-fire Target Acquisition (CTA) applications for semi-ballistic trajectories. 

Develop and demonstrate a material solution to provide adaptive, scalable, cost effective training and testing to improve the Command, Control, Communication, Computers, Cyber, Intelligence, Surveillance and Reconnaissance (C5ISR) system operations for the Army’s Signal Soldiers.

Design and develop a technology approach/solution to create and provide realistic multi-spectral (i.e. infrared (IR), radar, Identification of Friend/Foe (IFF), electro-magnetic (EM), etc.) and other visual Combat Identification signatures in support of the live fire training domain.

Research methodologies, frameworks, and processes to ingest, process, and correlate object detections and tracks from multiple imaging sensors including but not limited to ground, aerial, and overhead imagery and video.

Resolve biometric data issues in the current authoritative biometrics database, the Department of Defense’s Automated Biometric Identification System (DoD ABIS), through the development of a machine learning software application to identify errors and improve data quality, increasing speed and accuracy of responses to match requests. 

Design and build a biometric recognition camera system to be integrated with the pre-existing Automated Installation Entry (AIE) system for deployment at Army installation Access Control Points (ACPs).

Current Department of Defense force protection capabilities require the physical handling of Common Access Cards (CAC) and other DoD-issued identification cards. This process results in the physical handling of cards which are

Develop and demonstrate the capability to synthetically simulate infrared and radar signatures to trigger a threat or target response from the system under test for use in assessing automatic target detection, recognition, and identification in a fully immersive hardware-in-the-loop test environment. 

Develop and demonstrate a Comms capability and Multi-Function resource manager for the AN/TPQ-53 radar system. The AN/TPQ-53 radar system detects, classifies, tracks, and determines the location of enemy indirect fire in either 360- or 90-degree modes.