SWRA774 may   2023 IWRL6432

 

  1.   1
  2.   Trademarks
  3. 1Introduction
  4. 2Machine Learning in mmWave Sensing
  5. 3Development Process Flow
  6. 4Case-Study-1: Motion Classification
  7. 5Case-Study-2: Gesture Recognition
  8. 6References

References

  1. IWRL6432 data sheet
  2. Fundamentals of mmWave Sensing, mmWave Training Series
  3. S. Z. Gurbuz and M. G. Amin, "Radar-Based Human-Motion Recognition With Deep Learning: Promising Applications for Indoor Monitoring," in IEEE Signal Processing Magazine, vol. 36, no. 4, pp. 16-28, July 2019.
  4. M. E. Yanik and S. Rao, “Radar-Based Multiple Target Classification in Complex Environments Using 1D-CNN Models,” to appear in 2023 IEEE Radar Conference (RadarConf'23), San Antonio, USA, May 2023.
  5. P. Goswami, S. Rao, S. Bharadwaj and A. Nguyen, "Real-Time Multi-Gesture Recognition using 77 GHz FMCW MIMO Single Chip Radar," 2019 IEEE International Conference on Consumer Electronics (ICCE), 2019, pp. 1-4.75