Authors:
Ying Zhao
;
Tony Kendall
and
Bonnie Johnson
Affiliation:
Naval Postgraduate School, United States
Keyword(s):
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.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Business Intelligence Applications
;
Computational Intelligence
;
Data Analytics
;
Data Engineering
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
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.