Big Data Architecture and Analytics for Common Tactical Air Picture (CTAP)

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dc.contributor.authorZhao, Ying
dc.contributor.authorKendall, Walter
dc.contributor.authorYoung, Bonnie
dc.contributor.authorGermershauser, Zachary
dc.contributor.corporateNaval Research Program (NRP)
dc.contributor.corporateNaval Research Program
dc.contributor.otherInformation Sciences (IS)
dc.contributor.otherSystems Engineering
dc.contributor.otherGSBPP
dc.date10/1/2014 to 4/30/2015
dc.date.accessioned2018-04-03T23:21:39Z
dc.date.available2018-04-03T23:21:39Z
dc.date.issued2015
dc.description.abstractThe amount of data generated by intelligence, surveillance, and reconnaissance (ISR) sensors has become an overwhelming challenge for decision makers involved in CID and the CTAP environment that Big Data architecture and Analytics (BDAA could address. The Navy needs to apply new architectures and analytics such as the current state-of-the- art BDAA). We will show how Big Data Architectures and Analytics (BADD) can collect and analyze the rising tide of sensor Information and fuse it in a timely manner to enhance Common Tactical Air Picture (CTAP). For instance, more specifically, accurate, relevant and timely Combat Identification (CID) enables the warfighter to locate and identify critical airborne targets with high precision, optimizes the use of long-range weapons, aids in fratricide reduction, enhances battlefield situational awareness, and reduces exposure of U.S. Forces to enemy fire. Specifically we want to study 1) how to identify and assess the current CID methods, for example, the best combination of platforms, sensors, networks, and data including organic sensors, regional sensors and National Technical Means (NTM) that can be used for track correlation and continuity of CID and correlate IDs to tracks regarding the state- of-the-art of the systems, applications, databases from Navy, Joint and National programs. 2) how to use massive parallel and distributed computation to improve CID’s fidelity and latency reductions through recursive data fusion; 3) how to use learning agents to discover and learn the patterns in historical data and correlate the patterns with real-time data to detect anomaly; 4) how the discovered patterns might be consistent with all functional elements and existing rules for the Cooperative and Non-cooperative CID methods. 5) how to address the unique challenges of CID (e.g., extremely short dwell time for fusion, decision making, and targeting; uncertain or missing data outside sensor [e.g., radar, radio] ranges; and limited resources in air); 6) Improve real-time targeting recommendations and decision making towards a future vision of automated battleforce management.en_US
dc.description.sponsorshipNaval Research Programen_US
dc.description.sponsorshipPrepared for: Topic Sponsor OPNAV N2/N6 F33; Sponsor POC Jim Miller, CDR, USN & Mr. William Treadway, Integrated Fires Conceptsen_US
dc.identifier.urihttps://hdl.handle.net/10945/57688
dc.publisherMonterey, California. Naval Postgraduate Schoolen_US
dc.relation.ispartofseriesSelected Student Publications
dc.relation.ispartofseriesNaval Research Program (NRP) Project Documents
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleBig Data Architecture and Analytics for Common Tactical Air Picture (CTAP)en_US
dc.typeReporten_US
dspace.entity.typePublication
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