ARCHARITHMS INC — Department of Defense SBIR Phase I: N191-020

Description

Data stream analytics have been used in industry for a number of years to solve various logistical and other problems using on-the-fly monitoring, data-mining, and analysis of ongoing information streams. Recent advances in both Artificial Intelligence (AI) and high-speed parallel computing architectures may now provide the ability to execute such data-stream analysis algorithms in real time. U.S. Navy ships and personnel must operate in diverse environments (littoral, open sea) and are faced with the most complex and crowded battle spaces that exist. Threats and Blue assets can be ballistic, air-breathing, surface, and subsurface, and can be singles or multiples in quantity. Blue operations in the area can be frequent, varied, and dense. Due to these complexities, the challenges and criticality associated with reliable identification of observed objects are significant. The effectiveness of target identification (Target ID) impacts the ability to efficiently negate threats and the safety of Blue assets and personnel. This challenge, coupled with a future enhanced build of the AEGIS Combat System (ACS) presents an opportunity to architect, design, and implement a Target ID capability that meets the challenges.