Deep Learning for Radar and Communications

Next generation RF receivers have to operate in harsh environments where interference of all kinds increase the system level challenges. These types of receivers have numerous applications including cognitive radar, software-defined radio, and more generally, systems that require efficient spectrum management.

In this webinar, we will demonstrate techniques to apply Deep Learning and Machine Learning networks for a range of radar and wireless communications systems. The webinar will cover:

• Trade-offs between machine learning and deep learning workflows
• Data collection and labeling from off-the-shelf radars and software-defined radios to train and test classifiers
• Data synthesis to train Deep Learning and Machine Learning networks
• Efficient ways to work with radar and communications I/Q signals, including feature extraction techniques to improve classification results
• Application examples with Radar RCS and micro-Doppler identification, radar/comms waveform modulation ID, RF Fingerprinting, and 5G channel estimation

Presenter Bio:

Rick Gentile focuses on Phased Array, Signal Processing, Sensor Fusion, and RF applications at MathWorks. Prior to joining MathWorks, Rick was a Systems Engineer at MITRE and MIT Lincoln Laboratory, where he worked on the development of radar systems. Rick also was a DSP Applications Engineer at Analog Devices where he led embedded processor and system level architecture definitions for high performance signal processing systems. Rick co-authored the text “Embedded Media Processing”. He received a B.S. in Electrical and Computer Engineering from the University of Massachusetts, Amherst and an M.S. in Electrical and Computer Engineering from Northeastern University, where his focus areas of study included Microwave Engineering, Communications and Signal Processing.