  Organizations are starting to realize of the combined power of data and
data-driven algorithmic models to gain insights, situational awareness, and
advance their mission. A common challenge to gaining insights is connecting
inherently different datasets. These datasets (e.g. geocoded features, video
streams, raw text, social network data, etc.) per separate they provide very
narrow answers; however collectively they can provide new capabilities. In this
work, we present a data fusion framework for accelerating solutions for
Processing, Exploitation, and Dissemination (PED). Our platform is a collection
of services that extract information from several data sources (per separate)
by leveraging deep learning and other means of processing. This information is
fused by a set of analytical engines that perform data correlations, searches,
and other modeling operations to combine information from the disparate data
sources. As a result, events of interest are detected, geolocated, logged, and
presented into a common operating picture. This common operating picture allows
the user to visualize in real time all the data sources, per separate and their
collective cooperation. In addition, forensic activities have been implemented
and made available through the framework. Users can review archived results and
compare them to the most recent snapshot of the operational environment. In our
first iteration we have focused on visual data (FMV, WAMI, CCTV/PTZ-Cameras,
open source video, etc.) and AIS data streams (satellite and terrestrial
sources). As a proof-of-concept, in our experiments we show how FMV detections
can be combined with vessel tracking signals from AIS sources to confirm
identity, tip-and-cue aerial reconnaissance, and monitor vessel activity in an
area.
