Artificial Intelligence/Machine Learning

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. 

Develop and demonstrate alternative extended range cannon artillery (ERCA) precision guidance mechanism utilizing the AN/TPQ-53 radar system resulting in increased firing accuracies and improved hit-to-kill performance.

Develop a capability to help the MQ-1C Gray Eagle (a medium-altitude, long-endurance unmanned aircraft system) Payload Operators improve situational awareness (SA) and increase intelligence gathering efficiency to ultimately reduce workload.

The Apache Attack Helicopter Project Management Office (PMO) and the Sensors Product Director (PD) is seeking to develop an optimized Stationary Target Indicator (STI) waveforms with a supporting modeling and simulation tool set.

Investigate and develop a smart digitized map populated with threat data collected by a network of target geo-location devices that can work in a GPS or GPS-denied/degraded environment for legacy and next generation handheld detectors, and for explosive breacher mine detection for targeted neutralization of the explosive hazard threat.