Wednesday, 27 Oct, 1:30 PM – 3:00 PM


Target identification for radar applications is an area that is gaining attention. To identify targets, we need to classify the radar waveforms. When using Machine Learning and Deep Learning for the classification a common preprocessing step is to extract meaningful features from the raw data, e.g. using time frequency analysis, which can be input to a classifier. While effective, this procedure can require effort and domain knowledge to yield an accurate identification. 


In this webinar, we will demonstrate data synthesis techniques and train Machine Learning and Deep Learning networks for radar applications. The presentation will explain: 

  • 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

To showcase the possibilities and challenges with using Machine Learning and Deep Learning for target identification we will look at two application examples: 

  • Radar RCS identification 
  • Micro-Doppler signatures (for example, pedestrians, bicycles, aircraft with rotating blades)


  • Deep Learning applied to two Radar Examples
  • Radar specific tools for preprocessing
  • Generating Synthetic Data through Simulation

Webinar/Workshop organized by: MathWorks

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