Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
143 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Pedestrian Recognition with Radar Data-Enhanced Deep Learning Approach Based on Micro-Doppler Signatures (2306.08303v1)

Published 14 Jun 2023 in eess.SP, cs.CV, and cs.LG

Abstract: As a hot topic in recent years, the ability of pedestrians identification based on radar micro-Doppler signatures is limited by the lack of adequate training data. In this paper, we propose a data-enhanced multi-characteristic learning (DEMCL) model with data enhancement (DE) module and multi-characteristic learning (MCL) module to learn more complementary pedestrian micro-Doppler (m-D) signatures. In DE module, a range-Doppler generative adversarial network (RDGAN) is proposed to enhance free walking datasets, and MCL module with multi-scale convolution neural network (MCNN) and radial basis function neural network (RBFNN) is trained to learn m-D signatures extracted from enhanced datasets. Experimental results show that our model is 3.33% to 10.24% more accurate than other studies and has a short run time of 0.9324 seconds on a 25-minute walking dataset.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (25)
  1. D. Wachtel, J. Edler, S. Schröder, S. Queiroz, and W. Huber, “Convolutional neural network classification of vulnerable road users based on micro-doppler signatures using an automotive radar,” in 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC).   IEEE, 2022, pp. 866–872.
  2. O. Burkacky, J. Deichmann, G. Doll, and C. Knochenhauer, “Rethinking car software and electronics architecture,” McKinsey & Company, p. 11, 2018.
  3. Y. Xiang, Y. Huang, H. Xu, G. Zhang, and W. Wang, “A multi-characteristic learning method with micro-doppler signatures for pedestrian identification,” in 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC).   IEEE, 2022, pp. 3794–3799.
  4. W. Zhang, H. Li, G. Sun, and Z. He, “Enhanced detection of doppler-spread targets for fmcw radar,” IEEE Transactions on Aerospace and Electronic Systems, vol. 55, no. 4, pp. 2066–2078, 2019.
  5. V. C. Chen, F. Li, S.-S. Ho, and H. Wechsler, “Micro-doppler effect in radar: phenomenon, model, and simulation study,” IEEE Transactions on Aerospace and electronic systems, vol. 42, no. 1, pp. 2–21, 2006.
  6. J. A. Nanzer, “A review of microwave wireless techniques for human presence detection and classification,” IEEE Transactions on Microwave Theory and Techniques, vol. 65, no. 5, pp. 1780–1794, 2017.
  7. E. Tavanti, A. Rizik, A. Fedeli, D. D. Caviglia, and A. Randazzo, “A short-range fmcw radar-based approach for multi-target human-vehicle detection,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–16, 2021.
  8. S. Hor, N. Poole, and A. Arbabian, “Single-snapshot pedestrian gait recognition at the edge: A deep learning approach to high-resolution mmwave sensing,” in 2022 IEEE Radar Conference (RadarConf22).   IEEE, 2022, pp. 1–6.
  9. R. Ferguson, M. Chevrier, and A. Rankin, “mmwave radar: Enabling greater intelligent autonomy at the edge,” Texas Instruments, pp. 1–9, 2018.
  10. P. Cao, W. Xia, M. Ye, J. Zhang, and J. Zhou, “Radar-id: human identification based on radar micro-doppler signatures using deep convolutional neural networks,” IET Radar, Sonar & Navigation, vol. 12, no. 7, pp. 729–734, 2018.
  11. S. Abdulatif, F. Aziz, K. Armanious, B. Kleiner, B. Yang, and U. Schneider, “Person identification and body mass index: A deep learning-based study on micro-dopplers,” in 2019 IEEE Radar Conference (RadarConf).   IEEE, 2019, pp. 1–6.
  12. Y. Lang, Q. Wang, Y. Yang, C. Hou, Y. He, and J. Xu, “Person identification with limited training data using radar micro-doppler signatures,” Microwave and Optical Technology Letters, vol. 62, no. 3, pp. 1060–1068, 2020.
  13. Y. Kim, S. Ha, and J. Kwon, “Human detection using doppler radar based on physical characteristics of targets,” IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 2, pp. 289–293, 2014.
  14. M. S. Seyfioğlu, A. M. Özbayoğlu, and S. Z. Gürbüz, “Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities,” IEEE Transactions on Aerospace and Electronic Systems, vol. 54, no. 4, pp. 1709–1723, 2018.
  15. B. Vandersmissen, N. Knudde, A. Jalalvand, I. Couckuyt, A. Bourdoux, W. De Neve, and T. Dhaene, “Indoor person identification using a low-power fmcw radar,” IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 7, pp. 3941–3952, 2018.
  16. X. Qiao, Y. Feng, T. Shan, and R. Tao, “Person identification with low training sample based on micro-doppler signatures separation,” IEEE Sensors Journal, vol. 22, no. 9, pp. 8846–8857, 2022.
  17. P. Weller, F. Aziz, S. Abdulatif, U. Schneider, and M. F. Huber, “A mimo radar-based few-shot learning approach for human-id,” in 2022 30th European Signal Processing Conference (EUSIPCO).   IEEE, 2022, pp. 1796–1800.
  18. A. Creswell, T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta, and A. A. Bharath, “Generative adversarial networks: An overview,” IEEE signal processing magazine, vol. 35, no. 1, pp. 53–65, 2018.
  19. M. Rahman, S. Gurbuz, and M. Amin, “Physics-aware generative adversarial networks for radar-based human activity recognition,” IEEE Transactions on Aerospace and Electronic Systems, 2022.
  20. I. Alnujaim, S. S. Ram, D. Oh, and Y. Kim, “Synthesis of micro-doppler signatures of human activities from different aspect angles using generative adversarial networks,” IEEE Access, vol. 9, pp. 46 422–46 429, 2021.
  21. Y. Xie, E. Franz, M. Chu, and N. Thuerey, “tempogan: A temporally coherent, volumetric gan for super-resolution fluid flow,” ACM Transactions on Graphics (TOG), vol. 37, no. 4, pp. 1–15, 2018.
  22. Z. Cui, W. Chen, and Y. Chen, “Multi-scale convolutional neural networks for time series classification,” arXiv preprint arXiv:1603.06995, 2016.
  23. F. Girosi and T. Poggio, “Networks and the best approximation property,” Biological cybernetics, vol. 63, no. 3, pp. 169–176, 1990.
  24. T. Poggio and F. Girosi, “Networks for approximation and learning,” Proceedings of the IEEE, vol. 78, no. 9, pp. 1481–1497, 1990.
  25. L. Hu, W. Wang, Y. Xiang, and J. Zhang, “Flow field reconstructions with gans based on radial basis functions,” IEEE Transactions on Aerospace and Electronic Systems, vol. 58, no. 4, pp. 3460–3476, 2022.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now