Proceedings Article | 20 May 2015
Danielle Duff, Tien Pham, Ronald Rudnicki, Gary Stolovy, Jeff Houser, Alex James, Eric Nagler, Dorothy Hines, Keith Riser, Robert Ganger, James Schoening
KEYWORDS: Sensors, Biometrics, Intelligence systems, Data storage, Intelligent sensors, Unmanned aerial vehicles, Data fusion, Neodymium, Unattended ground sensors, Data processing
In the Army Intelligence domain, Processing, Exploitation, and Dissemination (PED) is the process that is used to convert and collect information into actionable intelligence and then distribute this intelligence appropriately to those who make decisions and execute the tasks and missions that this intelligence process supports. In today’s Intelligence domain, information is gathered from an abundance of sources and these sensors create an exponential amount of data output. PED is a time sensitive process, which is also constrained by manpower and the extremely limited tactical bandwidth. Currently, PED is primarily a higher echelon activity, but as information gathering increases at the platforms it makes sense to automate PED tasks and execute these tasks closer to the sensor sources. Providing an architecture that will allow for processing sensor data more intelligently at various locations within the Intel network to include: on-board a UAV or vehicle, COIST, and higher echelons can help to alleviate these constraints by positioning the sensor fusion as close as possible to minimize bandwidth utilization. However, this architecture will implicitly need a way to share data to enable fusion. While any given mission may require fusion of just a few sensor data sources, which can be accomplished with point-to-point integration, this approach does not scale and is not maintainable, since the range of all missions will require a combination of any number of data sources and this approach will most likely require extra development to handle new sources. Therefore, there needs to be a way to share and reuse date that is extensible, maintainable, and not tied to any one mission type. This approach will reduce duplication, provide common patterns for accessing information and support future growth. This paper describes how a common ontology can be used to transform intelligence data from any number of disparate sources to a higher level of integration where one uses the logical understanding of the domain to share knowledge between sources. This paper will discuss sensor ontology efforts to date, introduce the Common Core Ontologies which provide the common upper and mid-level semantics which are inherited by domain level ontologies and describe future experimentation. The paper will discuss the role of the Common Core Ontologies development and governance practices in producing a logically consistent data set, which can be accessed through a single API. By utilizing this approach, sensor outputs can be fused using inferencing, entity and event resolution, and other 3rd party analytic apps. Finally, the paper will also describe how ontologies are leveraged to enable tasking, analytics, rules based reasoning, and distributed processing which are functional components currently being utilized or developed to support the PED process.
