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Object classification on noise-reduced and augmented micro-doppler radar spectrograms

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Abstract

The classification of targets is one of the most challenging tasks in radar signal processing. Classifying a target can help radar operators figure out the nature of the target, such as its source and activity. However, it is very difficult to find the labeled data necessary to develop radar target classification models. Generating a radar dataset is an expensive and time-consuming process. To address these issues, we propose a noise reduction method that can be applied to micro-Doppler radar datasets. This method is carried out by averaging the spectrogram of each class in the RadEch micro-Doppler radar datasets and subtracting pixel by pixel from each sample. RadEch dataset has also been augmented with traditional and learning-based data augmentation methods. The learning-based data augmentation method was carried out by using Generative Adversarial Networks. Raw spectrograms, augmented spectrograms and noise-reduced spectrograms have been classified using 5-layer Convolutional Neural Network, VGG-16, and VGG-19. Classification results are compared with state-of-the-art studies. Comparison results show that the classification on noise-reduced spectrogram performs better than current state-of-the-art methods.

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References

  1. Nag S, Barnes MA, Payment T, Holladay G, (2002) “Ultrawideband through-wall radar for detecting the motion of people in real time”, In Proc. SPIE Radar Sensor Technol. Data Vis, vol 4744, p 48–57.

  2. Hunt AR, (2004) “A wideband imaging radar for through-the-wall surveillance,” In Proc. SPIE Sensors, Command, Control, Commun, Intell. (C3I) Technol, vol 5403, p 590–596.

  3. J. L. Geisheimer, E. F. Greneker, and W. S. Marshall, (2002) “High-resolution Doppler model of the human gait,” In Proc. SPIE Radar Sensor Technol. Data Vis, vol 4744, p 8–18.

  4. van Dorp P, Groen FCA (2003) Human walking estimation with radar. Proc Inst Elect Eng, Radar Sonar Navig 150(5):356–365

    Article  Google Scholar 

  5. M. Otero, (2005) “Application of a continuous wave radar for human gait recognition,” In Proc. SPIE Signal Process., Sensor Fusion, Target Recog., vol 5809, p 538–548.

  6. Li J, Ling H (2003) ISAR feature extraction from non-rigid body targets using adaptive chirplet signal representation. Proc Inst Elect Eng Radar, Sonar Navig 150:284–291

    Article  Google Scholar 

  7. Thayaparan T, Abrol S, Riseborough E, Stankovic L, Lamothe D, Duff G (2007) Analysis of radar micro-doppler signatures from experimental helicopter and human data. IET Radar, Sonar Navig 1(4):289–299

    Article  Google Scholar 

  8. P. Setlur, M. Amin, and F. Ahmad, (2007) “Urban target classifications using time-frequency micro-Doppler signatures,” In Proc. 9th Int. Symp. Signal Process. Appl, p 1–4.

  9. A. G. Stove and S. R. Sykes, (2002) “A Doppler-based automatic target classifier for a battlefield surveillance radar,” In Proc. IEEE Radar Conf. p 419–423.

  10. Seyfioğlu MS, Özbayoğlu AM, Gürbüz SZ (2018) Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities. IEEE Trans Aerosp Electron Syst 54(4):1709–1723

    Article  Google Scholar 

  11. Groot S et al (2013) Human motion classification using a particle filter approach: multiple model particle filtering applied to the micro-doppler spectrum. Int J Microw Wireless Technol 5:391–399

    Article  Google Scholar 

  12. J. Li et al. (2012) “Automatic classification of human motions using doppler radar,” In Proc. IEEE IJCNN, p 1–6.

  13. Kim Y, Ling H (2009) Human activity classification based on micro-doppler signatures using a support vector machine. IEEE Trans Geosci Remote Sens 47(5):1328–1337. https://doi.org/10.1109/TGRS.2009.2012849

    Article  Google Scholar 

  14. van Eden WD, de Villiers JP, Berndt RJ, Nel WAJ, Blasch E (2018) Micro-doppler radar classification of humans and animals in an operational environment. Expert Systems with Applications 102:1–11

    Article  Google Scholar 

  15. Chen VC et al (2006) Micro-doppler effect in radar: phenomenon, model, and simulation study. IEEE Trans Aerosp Electron Sys 42(1):2–21

    Article  Google Scholar 

  16. Chen VC, “Analysis of Radar Micro-Doppler Signature with Time-Frequency Transform,” Proc. of the IEEE Workshop on Statistical Signal and Array Processing (SSAP), Pocono, PA, 2000, pp. 463–466.

  17. Tivive FHC, Phung SL, Bouzerdoum A (2013) An image-based approach for classification of human micro-doppler radar signatures’’. Proc SPIE 8734:34–45

    Google Scholar 

  18. Molchanov P, Astola J, Egiazarian K, Totsky A, (2012) Frequency and phase coupling phenomenon in micro-Doppler radar signature of walking human, In Proc. 13th Int. Radar Symp, p 49–53.

  19. Al-Saffar AAM, Tao H, Talab MA, (2017)‘‘Review of deep convolution neural network in image classification,’’ In Proc. Int. Conf. Radar, Antenna Microw. Electron. Telecommun. (ICRAMET), p 26–31.

  20. Kim Y, Moon T (2016) ‘Human detection and activity classification based on micro-doppler signatures using deep convolutional neural networks.’ IEEE Geosci Remote Sens Lett 13(1):8–12

    Article  Google Scholar 

  21. Lundén J, Koivunen V, (2016) ‘‘Deep learning for HRRP-based target recognition in multistatic radar systems,’’ In Proc. IEEE Radar Conf, p 1–6.

  22. Trommel RP, Harmanny RIA, Cifola L, Driessen JN (2016) Multi-target human gait classification using deep convolutional neural networks on micro-doppler spectrograms. Eur Radar Conf (EuRAD) 2016:81–84

    Google Scholar 

  23. Kim BK, Kang H-S, Park S-O (2017) ‘Drone classification using convolutional neural networks with merged doppler images.’ IEEE Geosci Remote Sens Lett 14(1):38–42

    Article  Google Scholar 

  24. Park J, Javier RJ, Moon T, Kim Y (2016) ‘Micro-doppler based classification of human aquatic activities via transfer learning of convolutional neural networks.’ Sensors 16(12):1990

    Article  Google Scholar 

  25. Alhadhrami E, Al-Mufti M, Taha B, Werghi N (2019) Learned micro-doppler representations for targets classification based on spectrogram images. IEEE Access 7:139377–139387. https://doi.org/10.1109/ACCESS.2019.2943567

    Article  Google Scholar 

  26. M. S. Andrić, B. P. Bondžulić, and B. M. Zrnić, ( 2010) ‘‘The database of radar echoes from various targets with spectral analysis,’’ In Proc. 10th Symp. Neural Netw Appl Elect Eng, p 187–190.

  27. Simonyan, Karen and Zisserman, Andrew. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv 1409.1556.

  28. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L, (2009). Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition (pp 248–255).

  29. Krizhevsky A, Sutskever I, Hinton G (2012) ImageNet classification with deep convolutional neural networks. Neural Inf Process Sys. https://doi.org/10.1145/3065386

    Article  Google Scholar 

  30. Russakovsky O, Deng J, Hao S, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2015) ImageNet large scale visual recognition challenge. Int J Comput Vis 115(3):211–52

    Article  MathSciNet  Google Scholar 

  31. Hadhrami EA, Mufti MA, Taha B, Werghi N (2018) Transfer learning with convolutional neural networks for moving target classification with micro-doppler radar spectrograms. Int Conf Artif Intell Big Data (ICAIBD) 2018:148–154. https://doi.org/10.1109/ICAIBD.2018.8396184

    Article  Google Scholar 

  32. Zabalza J, Clemente C, Di Caterina G, Ren J, Soraghan JJ, Marshall S (2014) Robust PCA micro-doppler classification using SVM on embedded systems. IEEE Trans Aerosp Electron Sys 50(3):2304–2310. https://doi.org/10.1109/TAES.2014.130082

    Article  Google Scholar 

  33. Bilik I, Tabrikian J, Cohen A (2006) Gmm-based target classification for ground surveillance doppler radar. Aerosp Electron Sys IEEE Trans on 42(1):267–278

    Article  Google Scholar 

  34. Molchanov P, Astola J, Egiazarian K, Totsky A, (2012) "Classification of Ground Moving Radar Targets by Using Joint Time-Frequency Analysis," 2012 IEEE Radar Conference, Atlanta, GA, pp. 0366-0371.

  35. Clemente C, Pallotta L, Maio AD, Soraghan JJ, Farina A (2015) A novel algorithm for radar classification based on doppler characteristics exploiting orthogonal pseudo-zernike polynomials. In IEEE Trans Aerosp Electron Sys 51(1):417–430

    Article  Google Scholar 

  36. Patel K, Rambach K, Visentin T, Rusev D, Pfeiffer M, Yang B (2019) Deep learning-based object classification on automotive radar spectra. IEEE Radar Conf (RadarConf) 2019:1–6. https://doi.org/10.1109/RADAR.2019.8835775

    Article  Google Scholar 

  37. Zhu J, Chen H, Ye W (2020) ‘A hybrid CNN–LSTM network for the classification of human activities based on micro-doppler radar.’ IEEE Access 8:24713–24720

    Article  Google Scholar 

  38. Erol B, Gurbuz SZ, Amin MG (2020) Motion classification using kinematically sifted ACGAN-synthesized radar micro-Doppler signatures. IEEE Trans Aerosp Electron Syst. https://doi.org/10.1109/TAES.2020.2969579

    Article  Google Scholar 

  39. Zhao R, Ma X, Liu X, Liu J (2021) An end-to-end network for continuous human motion recognition via radar radios. IEEE Sensors J 21(5):6487–6496

    Article  Google Scholar 

  40. Shrestha A, Li H, Le Kernec J, Fioranelli F (2020) ‘Continuous human activity classification from FMCW radar with bi-LSTM networks.’ IEEE Sensors J 20(22):13607–13619

    Article  Google Scholar 

  41. Sun Y et al (2021) Moving target localization and activity/gesture recognition for indoor radio frequency sensing applications. In IEEE Sens J 21(21):24318–24326. https://doi.org/10.1109/JSEN.2021.31111872

    Article  Google Scholar 

  42. Li H, Mehul A, Le Kernec J, Gurbuz SZ, Fioranelli F (2021) Sequential human gait classification with distributed radar sensor fusion. IEEE Sens J 21(6):7590–7603

    Article  Google Scholar 

  43. Lee D, Cheung C, Pritsker D (2019) Radar-based object classification using an artificial neural network. IEEE Natl Aerosp Electron Conf (NAECON) 2019:305–310. https://doi.org/10.1109/NAECON46414.2019.9058319

    Article  Google Scholar 

  44. Amina, Serir & Bouhafsi, Youcef. (2012). Micro-doppler radar signature classification by time-frequency and time-scale analysis. In 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA) https://doi.org/10.1109/ISSPA.2012.6310701.

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Acknowledgements

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. We are grateful to Assoc. Prof. Radim Burget who has participated in the study and provided significant insights and supports.

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Authors and Affiliations

  1. Institute of Science and Engineering, Başkent University, 06790, Ankara, Turkey

    Alperen Erdoğan

  2. Department of Electrical and Electronics Engineering, Başkent University, 06790, Ankara, Turkey

    Selda Güney

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Erdoğan, A., Güney, S. Object classification on noise-reduced and augmented micro-doppler radar spectrograms. Neural Comput & Applic 35, 429–447 (2023). https://doi.org/10.1007/s00521-022-07776-3

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