Fusion of Multiple Motion Information Sources

Our approach to this Phase II program will address the four Vs of big data and the four pillars, or principles, of ABI.It includes multi-INT data association and fusion through a graph framework and is inclusive of images/objects and activities extracted from FMV streams.To address velocity and veracity of multi-INT data sources we propose to develop a scalable graph-based analytic approach which will perform near real-time analysis of multi-INT data sources.Kinematic, contextual, and feature data can be updated in the graph as it is collected and processed.The graph accounts for the uncertainty of data via soft or fuzzy associations without having to make the typical hard association decisions commonly used in the data fusion process.Our graph-based fusion and analytics approach is extendable to more focused analyses commonly encountered by analysts at NASIC and NGA.The inclusion of 4d geo-spatial analytics allow for visualization and analyst interaction with the graph.To address the variety and volume of data we propose to architect a distributed storage and processing system that integrates graph technology and will be an extension of the existing DREAD 2.0 system architecture that utilizes the Hadoop ecosystem inclusive of HDFS, MapReduce, and HBase.