March 2, 2020: A System-of-Systems Perspective on Information Fusion with Application to Command and Control Systems
- March 2, 2020
- 4:00 p.m.
- 100 Hancock Hall
- Dr. Ali Raz, Purdue Univeristy
- Faculty Host: Dr. Jonathan Black
Abstract: Complex and autonomous systems will soon be ubiquitous and transform the way we interact with the world. Current and future mission needs in both civilian and defense domains depend upon integration of multiple autonomous systems (to include air and space systems) with an ability to collectively generate, consume, and fuse information, e.g., a transportation system of autonomous vehicles, multi-domain command and control system for air and missile defense, and/or swarms of intelligent reconnaissance drones. These systems can be classified as an Information Fusion System-of-Systems (IF-SoS) which is comprised of distributed and heterogeneous information gathering, processing, and fusing elements that are integrated with one another and lead to enhanced situational awareness and decision-making capabilities. However, this distribution, heterogeneity, and complexity of information processes, along with the multiplicity of fusion functions, and evolving operational environment, creates an extensive design space for the IF-SoS. Traditionally, partitioned and isolated approaches are employed for the IF-SoS design where the different constituent elements are individually analyzed. Such approaches assume a lack of interdependence between system design decisions and remain contrary to complex systems principles as interdependence is the key for understanding emergent behavior.
Building on the recent developments in complex systems and system-of-systems research, in this presentation, I will discuss a Model-Based Systems Engineering (MBSE) framework for developing integrated IF-SoS architectures with a command and control system example. The integrated architectures of IF-SoS provide holistic and mission-focused representation and facilitate a common mathematical representation of design and operational variables. The resulting design space—developed as an agent-based model—remains extensively large and needs to be analyzed for interdependence. I will discuss machine learning techniques and statistical methods that allow comprehension of this extensive design space and help identify and quantify the impact of variations in the IF-SoS design space.
Furthermore, I will provide an overview of my on-going research projects on formulating artificial intelligence techniques for high-speed aerospace systems mission design and my plan for building the Integrated Autonomous Systems Science Engineering and Technology (iASSET) laboratory.
Bio: Dr. Ali Raz is a Visiting Assistant Professor at Purdue University School of Aeronautics and Astronautics. His research interests are in systems engineering, system-of-systems, and information fusion. He also holds a temporary faculty appointment with U.S. Navy Naval Surface Warfare Center in Crane, IN. He has worked with the John Hopkins University Applied Physics Laboratory on fusion systems and prior to joining Purdue University, he was a flight controls and flight management systems engineer at Honeywell Aerospace. He is a Certified Systems Engineering Professional from the International Council on Systems Engineering (INCOSE) where he is a co-chair of the Complex Systems Working Group and the assistant director of early career professionals. He is a senior member of the American Institute for Aeronautics and Astronautics (AIAA) and a senior member of the Institute of Electrical and Electronic Engineers (IEEE). He holds a Bachelor and Master of Science in Electrical Engineering from Iowa State University, and a Ph.D. in Aeronautics and Astronautics from Purdue University.