Big Data and Deep Analytics Applied to the Common Tactical Air Picture (CTAP) and Combat Identification (CID)
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Authors
Zhao, Ying
Kendall, Tony
Johnson, Bonnie
Subjects
Big Data
Deep Analytics
Common Tactical Air Picture
Combat Identification
Machine Vision
Object Recognition
Pattern Recognition
Anomaly Detection
Lexical Link Analysis
Heterogeneous Data Sources
Unsupervised Learning
Deep Analytics
Common Tactical Air Picture
Combat Identification
Machine Vision
Object Recognition
Pattern Recognition
Anomaly Detection
Lexical Link Analysis
Heterogeneous Data Sources
Unsupervised Learning
Advisors
Date of Issue
2016
Date
Publisher
SCITEPRESS – Science and Technology Publications
Language
Abstract
Accurate combat identification (CID) enables warfighters to locate and identify critical airborne objects as
friendly, hostile or neutral with high precision. The current CID processes include processing and analysing
data from a vast network of sensors, platforms, and decision makers. CID plays an important role in
generating the Common Tactical Air Picture (CTAP) which provides situational awareness to air warfare
decision-makers. The Big “CID” Data and complexity of the problem pose challenges as well as
opportunities. In this paper, we discuss CTAP and CID challenges and some Big Data and Deep Analytics
solutions to address these challenges. We present a use case using a unique deep learning method, Lexical
Link Analysis (LLA), which is able to associate heterogeneous data sources for object recognition and
anomaly detection, both of which are critical for CTAP and CID applications.
Type
Description
The article of record as published may be located at http://dx.doi.org/10.5220/0006086904430449
Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2016) - Volume 1: KDIR, pages 443-449
Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2016) - Volume 1: KDIR, pages 443-449
Series/Report No
Department
Operations Research
Organization
Identifiers
NPS Report Number
Sponsors
OPNAV Combat Identification Capability Organization.
Funder
Naval Postgraduate School Research Program
Format
Citation
Distribution Statement
Rights
This 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.