Target Identification Interrogation Data Stream Analytics System

In a combat scenario, the ability to adapt to new information quickly and effectively provides an opportunity to outmaneuver adversaries and protect allied assets and resources. A real-time framework provides the infrastructure needed to rapidly ingest newly acquired sensor data and distill the information to key metrics to present to a combat system operator. With a capable real-time combat system, combat performance is greatly enhanced through the ability to quickly process and present new information for an operator, increasing the speed of the command loop.In Phase I, A Real-Time Streaming Analytics Platform (RT-SAP) that enables Track ID algorithms from a data stream including support for algorithms based on deep learning solutions will be developed. In a communications/sensor denied environment where information from traditional transponder-based Track ID data, radio frequency and voice interrogation of the ambiguous air track are unavailable, a predictive Track ID capability based on any available information is a critical asset. The combination of a predicted class based on objective observations from sensor data with an operator’s higher-level understanding of the current tactical situation (TACSIT) and Area of Responsibility (AOR) allow for a collective decision between man and machine to maximize track ID performance.