This seminar will focus on modern electronic support (ES) and electronic attack (EA) techniques using deep learning methods. In the afternoon session, we begin with an examination of persistent spectrum sensing and signal detection methods starting with an examination of both centralized and decentralized electromagnetic spectrum sensing techniques. We will continue with an overview of how artificial intelligence (AI) and machine learning (ML) techniques are being used in transceivers for EMW. This includes model and data parallel training techniques for both centralized and decentralized architectures.
Registartion Link:
[Developing Digital RF Memories and Transceiver Technologies for Electromagnetic Warfare Seminar (rohde-schwarz.com)](https://events.rohde-schwarz.com/profile/form/index.cfm?PKformID=0x21566abcd&source=IEEE)

We follow with an overview on important deep learning techniques that go beyond neural networks (NN). These include autoencoders, convolutional NN, long-, short-term memories, restricted and deep Boltzmann machines and generative adversarial networks. We focus on their use for EA applications including how DRFM and UAVs in a tactical environment can develop a deep learning control strategy. Transfer learning and the importance of federated learning is also addressed. Different types of EA applications include obscuration-EA, deception-EA, and target-EA. We conclude with an examination of the proper clutter coefficients for deception

Co-sponsored by: RHODES and SCWARTZ, AOC and CACI

Speaker(s): Dr. Philip Pace,

Agenda:
Date:
Monday, November 7th, 2022
Time:
3:00pm to 9:00pm EST
Please arrive 30 min early for registration.
Location:
CACI
12 Vreeland Rd – Cafeteria
Florham Park, NJ 07932

Registartion link:

[Developing Digital RF Memories and Transceiver Technologies for Electromagnetic Warfare Seminar (rohde-schwarz.com)](https://events.rohde-schwarz.com/profile/form/index.cfm?PKformID=0x21566abcd&source=IEEE)

CACI 12 Vreeland Rd – Cafeteria, Florham Park,, New Jersey, United States, 07932