Summaries - Office of Research & Innovation
Back Big Data Architecture and Analytics for Common Tactical Air Picture
|Division||Research & Sponsored Programs|
|Department||Naval Research Program|
|Sponsor||NPS Naval Research Program (Navy)|
We propose a study to investigate a big data analytics that can analyze the rising tide of sensor Information to fuse it in a timely manner to enhance the fidelity and granularity of the tactical air picture. The amount of data generated by intelligence, surveillance, and reconnaissance (ISR) sensors has become overwhelming. Navy has needs to apply new architectures and analytics to better a common tactical air picture (CTAP) to support Integrated Air and Missile Defense (IAMD) missions, including Naval Integrated Fire Control-Counter Air (NIFC-CA). This decision space requires that information be presented in timely manner with the highest confidence level practical. The proposed study will focus on the following tasks:
Task 1: Identify/assess the current CTAP, identify key elements required to support IAMD and NIFC-CA, for example, the best combination of platforms, sensors, networks, and data in a common tactical air picture to include organic sensors, regional sensors and National Technical Means (NTM) that track correlation and continuity as well as support CID and correlate ID to tracks. This will include a trip for two researchers for briefings and classes if available to the sponsor's location to learn from domain experts regarding the state-of -the-art of the systems, applications, databases from Navy, Joint and National programs.
Task 2: Investigate/explore how to apply innovatively the big data architectures and related analytic platforms such as Lexical Learning Agents (LLA) jointly with hadoop, map/reduce, Distributed R, Rhadoop, Apache Mahout and Model View Controller (MVC) to construct a better CTAP. For example, 1) Use massive parallel computation to improve CID's fidelity and latency reductions; 2) Use learning agents to improve the correlations between real-time data with historical patterns, detect anomalies and reduce false alarms; 3) Address the unique challenges of CTAP (e.g., extremely short dwelt time for fusion, decision making, and targeting; uncertain or missing data outside sensor [e.g., radar, radio] ranges; and limited resources In air); 4) Improve real-time targeting recommendations and decision making towards a future vision of automated battleforce management.
The deliverables for this project will be CID and CTAP requirements to the Office of Naval Research (ONR)'s Future Naval Capabilities (FNC) Naval Tactical Cloud (NTC) project.
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