Building Datasets for AI-Enabled Radar, Communications, and EW Systems
In this talk, we will examine the use of MATLAB and Simulink for creating training datasets for AI-enabled radar, communication systems, and other RF emitters. These tools contribute to the effective training of deep learning and machine learning algorithms, which are essential for cognitive radio applications. Through an overview of various practical examples, attendees will gain a better understand for how they can begin to implement these technologies in the field of AI-enhanced RF systems.
Topics covered will include:
- How digital engineering helps interdisciplinary teams collaborate
- Considerations for how modeling and simulation can be performed at multiple abstraction levels
- An introduction to several multifunction RF mode-agility examples
- An AI workflow overview with a corresponding example
Please allow approximately 45 minutes to attend the presentation and Q&A session. We will be recording this webinar, so if you can't make it for the live broadcast, register and we will send you a link to watch it on-demand.
About the Presenters
Mike Rudolph – Aerospace and Defense Industry Manager, MathWorks
Michael Rudolph has been the Aerospace and Defense Industry Manager at MathWorks since 2019. In his role, he works with engineering leadership throughout the industry to understand ongoing and emerging technology trends from autonomous systems to multifunction RF systems. Before joining MathWorks, he spent a decade at Raytheon BBN Technologies performing interdisciplinary research on various sensor and RF systems for DoD customers like Defense Advanced Research Projects Agency (DARPA). He holds bachelor’s and master's degrees in engineering from Penn State University, and an MBA from University of Virginia's Darden School of Business.
Aram Vartanyan – Application Engineer, MathWorks
Aram Vartanyan is an Application Engineer at MathWorks specializing in topics related to radar, signal processing, and RF propagation. He received a B.S. degree in physics and mathematics and a Ph.D. in ionospheric plasma physics from the University of Maryland, College Park. Prior to MathWorks, Aram worked at the Johns Hopkins University Applied Physics Laboratory for seven years, where he worked on several topics, such as unique over-the-horizon radar modalities, nuclear effects analysis, and novel directed energy applications. His interests also include computational electromagnetics and the nexus of physics-based modeling and software development.