Deep Learning for Radar and Wireless Communications


Modulation identification and target classification are important functions for intelligent RF receivers. These functions have numerous applications in cognitive radar, software-defined radio, and efficient spectrum management. To identify both communications and radar waveforms, it is necessary to classify them by modulation type. For this, you can extract meaningful features which can be input to a classifier. While effective, this procedure can require effort and domain knowledge to yield an accurate identification. A similar challenge exists for target classification. 

In this webinar, we will demonstrate data synthesis techniques that can be used to train Deep Learning and Machine Learning networks for a range of radar and wireless communications systems including: 

  • Understanding data set trade-offs between machine learning and deep learning workflows
  • Efficient ways to work with 1D and 2D (time-frequency) signals
  • Feature extraction techniques that can be used to improve classification results
  • Application examples with Radar RCS identification, radar/comms waveform modulation ID, and Micro-Doppler signatures to help with target identification (for example, pedestrians, bicycles, aircraft with rotating blades)
  • Validation with over-the-air signals from software-defined radios (SDR) and radars.

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 Presenter

Rick Gentile focuses on Phased Array, Signal Processing, and Sensor Fusion 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, including automotive driver assist 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.

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