Chris of Data Fusion & Neural Networks

Christopher Bowman, PhD. 

Data Fusion & Neural Networks (DF&NN) 

Dr. Christopher Bowman is the 2018 Joseph Mignogna Data Fusion Award winner of the International Society of Information Fusion and creator of the Bowman Model developed in 1985 for the US Joint Directors of Laboratories (JDL) Data Fusion group.  Data Fusion is defined as the process of integrating multiple data sources to produce more consistent, accurate, and useful information than provided by any one source.  Sensor Fusion is a sub-set of Data Fusion.  One example of an application that might use Sensor Fusion would be using multiple sensors to track Unmanned Aerial Vehicles.  Dr. Bowman will give us a tutorial on the Data Fusion architecture.

The DNN Technical Architecture provides a standard functional decomposition of DF&RM software development point designs, interface descriptions, and engineering methodology to enable software tool reuse and reduce need for one-of-a-kind DF&RM systems. The goals of the presentation include:

• Provide an understanding of the roles for DF&RM
• Describe how the Data Fusion heritage can be used to “jump-start” dual Resource Management solutions 
• Describe DF&RM Dual Node Network (DNN) Technical Architecture 
• Provide Problem-to-Solution Mappings for Data Association 

Biography:

Dr. Bowman has proffered diverse computational techniques including intelligent systems, data and goal-driven techniques, Bayes Nets, possibilistic (fuzzy & evidential), symbolic (rules & scripts), and nonlinear pattern recognition neural networks (NN). On the SIGINT Science and Technology Advisory Board and Project Hercules for MDA he supervised an operational fusion system design & development. He was the originator of the DF&RM DNN technical architecture that supports affordable DF&RM synthesis, as well as comparative analyses. He was the lead on the development of the Enterprise Satellite as a Sensor (E-SAS) TRL 7 data-driven software development for abnormal State of Health (SOH) detection and characterization for Schriever AFB. He led the development and delivery of the Abnormal Catalog Update (ACU) detection and characterization, AFSCN Link Protection System (ALPS) Electro-magnetic Interference (EMI) detection, and GPS scintillation outage prediction, systems. He was the systems engineer for the AFRL JSARS program that developed space situation awareness tools to the Joint Space Operations Center (JSpOC). He received the Mignogna Data Fusion award. He holds the ANOM Abnormality Detection & Classification Patent: “Detecting, Classifying, and Tracking Abnormal Data in a Data Stream”. He has over 100 publications. 

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