Paper Info
Title
Big Data and Deep Analytics Applied to the Common Tactical Air Picture (CTAP) and Combat Identification (CID).
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.
Year
DOI
Venue
2016
10.5220/0006086904430449
KDIR
Keywords
Field
DocType
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
Data science,Data mining,Anomaly detection,Situation awareness,Computer science,Link analysis,Unsupervised learning,Artificial intelligence,Deep learning,Analytics,Big data,Cognitive neuroscience of visual object recognition
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Ying Zhao101.35
Tony Kendall201.01
Bonnie Johnson300.68