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Deep Learning for Radar Waveform Classification

Radar

This webinar is prerecorded

Free | online | Orkeny Zovathi


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Explore our on-demand webinar that delves into the application of deep learning for radar waveform classification using MATLAB and Simulink. This session is specifically tailored for engineers who are keen to leverage artificial intelligence for advanced radar signal processing.

In this webinar, we provide a comprehensive overview of MATLAB’s capabilities in applying AI techniques to radar waveform classification. Through a detailed case study, you will learn how to classify radar waveform types from generated synthetic data using the Wigner-Ville distribution (WVD) and a deep convolutional neural network (CNN).

Why Watch?

  • Understand the significance of modulation classification for intelligent receivers.
  • Discover how modulation classification can enhance applications such as cognitive radar and software-defined radio.
  • Learn about the challenges of traditional classification methods and how deep learning simplifies the process by automatically extracting time-frequency features from signals.

Key Takeaways:

  • An overview of various AI methods and solutions offered by MATLAB for radar signal classification.
  • Step-by-step guidance on developing an AI-based radar waveform classification solution in MATLAB.
  • Access to resources for further learning and development in the field of AI and radar signal processing.

Agenda:

  • Applications of AI for Radar
  • Developing an AI for Radar solution in MATLAB: Radar Waveform Classification
  • Resources for further learning

Watch this webinar at your convenience and gain valuable insights to enhance your expertise in radar technology. Register now to access the on-demand session!

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Deep Learning for Radar Waveform Classification

About the Speaker(s)


  • Orkeny Zovathi

    Orkeny Zovathi

    Application Engineer

    Orkeny is an application engineer at SciEngineer. His research and consulting work focus on AI and machine learning.

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This webinar is prerecorded. Register for an access link!
Free | online | Orkeny Zovathi

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