Startseite Technik Determination of torso and main limp features of human gait from micro-Doppler radar measurements based on envelope detection and Kalman filtering
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Determination of torso and main limp features of human gait from micro-Doppler radar measurements based on envelope detection and Kalman filtering

  • Onur Tekir ORCID logo EMAIL logo und Caner Özdemir ORCID logo
Veröffentlicht/Copyright: 15. September 2025
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Abstract

This paper proposes a practical method for detecting the dominant motion characteristics of human walk and run based on micro-Doppler radar measurements. The method consists of two stages: In the first stage, the main body, i.e. torso, characteristics are identified by the help of Kalman Filtering based detection algorithm. Then, the detected torso signature is extracted from the two-dimensional (2D) range-Doppler image such that the residual image contains signatures from other body parts. In the second stage of the method, a human-Doppler threshold based envelope detection routine is employed to identify the micro-Doppler signatures from dominant swinging limps; i.e. arms and/or legs. To demonstrate the effectiveness and the validity of the proposed techniques, two measured examples from real world experiments are presented. These examples show that the signatures of main body and swinging arms are successfully detected and classified with good fidelity such that the velocity of the torso and the period/speed values of the arms are precisely estimated for much better classification of the human motion thanks to the proposed Kalman Filtering based detection, and human-Doppler threshold based envelope detection routines.


Corresponding author: Onur Tekir, Department of Electrical-Electronics Engineering, Mersin University, Mersin, Türkiye, E-mail:

Funding source: Mersin University Scientific Research Unit

Award Identifier / Grant number: Project No. 2018-2-TP3-2924

Acknowledgments

Authors would like to thank Mr. Rasheed Khankan for his help during experiments.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: There is no conflict of this work with any other author or research. The authors declare no conflicts of interest regarding this article.

  6. Research funding: This work was supported by Mersin University Scientific Research Unit under Project No. 2018-2-TP3-2924.

  7. Data availability: There is no data or material is available separately.

References

[1] P. Withington, H. Fluhler, and S. Nag, “Enhancing homeland security with advanced UWB sensors,” IEEE Microwave Mag., vol. 4, no. 3, pp. 51–58, 2003, https://doi.org/10.1109/mmw.2003.1237477.Suche in Google Scholar

[2] V. Chen, The Micro-doppler Effect in Radar, Norwood, MA, USA, Artech House, 2011.Suche in Google Scholar

[3] V. C. Chen, D. Tahmoush, and W. J. Miceli, Radar Micro-doppler Signature Processing and Applications, London, IET Digital Library, 2014.10.1049/PBRA034ESuche in Google Scholar

[4] M. Otero, “Application of a continuous wave radar for human gait recognition,” in Signal Processing, Sensor Fusion, and Target Recognition XIV, vol. 5809, Bellingham, WA, USA, SPIE, 2005, pp. 538–548.10.1117/12.607176Suche in Google Scholar

[5] M. Zenaldin and R. M. Narayanan, “Radar micro-Doppler based human activity classification for indoor and outdoor environments,” in Proc. SPIE, Radar Sensor Technology XX, vol. 9829, Bellingham, WA, USA, SPIE, 2016.10.1117/12.2228397Suche in Google Scholar

[6] H. Sun, Z. Liu, and Q. Lin, “Radar target recognition based on micro-Doppler effect,” in Proceedings of an International Conference on Signal Processing, vol. 3, Beijing, China, 2006, pp. 651–656.10.1109/ICOSP.2006.345895Suche in Google Scholar

[7] V. Chen and H. Ling, Time-Frequency Transforms for Radar Imaging and Signal Analysis, Norwood, MA, Artech House, 2002.Suche in Google Scholar

[8] O. Tekir, B. Yılmaz, and C. Özdemir, “Signal preprocessing routines for the detection and classification of human micro-Doppler radar signatures,” Microw. Opt. Technol Lett., vol. 65, no. 8, pp. 2132–2149, 2023.10.1002/mop.33684Suche in Google Scholar

[9] D. Cunado, M. S. Nixon, and J. N. Carter, “Automatic extraction and description of human gait models for recognition purposes,” Comput. Vis. Image Understand., vol. 90, no. 1, pp. 1–41, 2003, https://doi.org/10.1016/s1077-3142(03)00008-0.Suche in Google Scholar

[10] A. R. Anwary, H. Yu, and M. Vassallo, “An automatic gait feature extraction method for identifying gait asymmetry using wearable sensors,” Sensors, vol. 18, no. 2, p. 676, 2018, https://doi.org/10.3390/s18020676.Suche in Google Scholar PubMed PubMed Central

[11] C. Hornsteiner and J. Detlefsen, “Characterisation of human gait using a continuous-wave radar at 24 GHz,” Adv. Radio Sci., vol. 6, pp. 67–70, 2008, https://doi.org/10.5194/ars-6-67-2008.Suche in Google Scholar

[12] D. Tahmoush and J. Silvious, “Time-integrated range-Doppler maps for visualizing and classifying radar data,” in IEEE RadarCon, Kansas City, MO, USA, IEEE, 2011, pp. 372–374.10.1109/RADAR.2011.5960562Suche in Google Scholar

[13] V. C. Chen, “Analysis of radar micro-Doppler with time-frequency transform,” in Proc. of the Tenth IEEE Workshop on Statistical Signal and Array Processing (Cat. No.00TH8496), Pocono Manor, PA, USA, IEEE, 2000, pp. 463–466.Suche in Google Scholar

[14] V. C. Chen, F. Li, S. S. Ho, and H. Wechsler, “Analysis of micro-doppler signatures,” IEE Proc-Radar Sonar Navig., vol. 150, no. 4, 2003, https://doi.org/10.1049/ip-rsn:20030743.10.1049/ip-rsn:20030743Suche in Google Scholar

[15] M. Nałęcz, “Micro-doppler analysis of signal received by FMCW radar,” in Proc. Int. Radar Symposium IRS 2003, Dresden, Germany, 2003, pp. 651–656.Suche in Google Scholar

[16] P. Van Dorp and F. C. A. Groen, “Feature-based human motion parameter estimation with radar,” IET Radar, Sonar Navig., vol. 2, no. No.2, pp. 135–145, 2008, https://doi.org/10.1049/iet-rsn:20070086.10.1049/iet-rsn:20070086Suche in Google Scholar

[17] C. Karabacak, S. G. Gurbuz, A. C. Gurbuz, M. B. Guldogan, G. Hendeby, and F. Gustafsson, “Knowledge exploitation for human micro-Doppler classification,” IEEE Geosci. Remote. Sens. Lett., vol. 12, no. 10, pp. 2125–2129, 2015, https://doi.org/10.1109/lgrs.2015.2452311.Suche in Google Scholar

[18] B. Lyonnet, C. Ioana, and M. G. Amin, “Human gait classification using microDoppler time-frequency signal representations,” in 2010 IEEE Radar Conference, Arlington, VA, USA, IEEE, 2010, pp. 915–919.10.1109/RADAR.2010.5494489Suche in Google Scholar

[19] Y. Kim and T. Moon, “Human detection and activity classification based on micro-doppler signatures using deep convolutional neural networks,” IEEE Geosci. Remote. Sens. Lett., vol. 13, no. 1, pp. 8–12, 2016, https://doi.org/10.1109/lgrs.2015.2491329.Suche in Google Scholar

[20] Y. He, P. Molchanov, T. Sakamoto, P. Aubry, F. L. Chevalier, and A. Yarovoy, “Range doppler surface: a tool to analyse human target in ultra-wideband radar,” IET Radar, Sonar Navig., vol. 9, no. 9, pp. 1240–1250, 2015, https://doi.org/10.1049/iet-rsn.2015.0065.Suche in Google Scholar

[21] P. Held, “Radar-based analysis of pedestrian micro-doppler signatures using motion capture sensors,” in 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, China, IEEE, 2018, pp. 787–793.10.1109/IVS.2018.8500656Suche in Google Scholar

[22] T. R. Saeed, S. Mutashar, A. Abed, M. Al-Muifraje, A. Shuraiji, and S. Abd-Elghany, “Human motion detection through wall based on micro-Doppler using kalman filter combined with convolutional neural network,” Int. J. Intell. Eng. Syst., vol. 12, no. 4, pp. 317–327, 2019, https://doi.org/10.22266/ijies2019.0831.29.Suche in Google Scholar

[23] T. R. Saeed, M. H. Al-Muifraje, J. W. A. Sadah, and A. N. Abed, “Moving target tracking and classification based on micro-Doppler signatures,” Int. J. Intell. Eng. Syst., vol. 12, no. 3, pp. 118–128, 2019, https://doi.org/10.22266/ijies2019.0630.13.Suche in Google Scholar

[24] P. Vaishnav and A. Santra, “Continuous human activity classification with unscented kalman filter tracking using FMCW radar,” IEEE Sens. Lett., vol. 4, no. 5, pp. 1–4, 2020, Art no. 7001704, https://doi.org/10.1109/lsens.2020.2991367.Suche in Google Scholar

[25] MathWorks, “MATLAB Release R2019a, Natick, MA, USA: The MathWorks Inc., 2019,” in MATLAB Release R2019a, Natick, Massachusetts, USA, MathWorks Inc., 2019.Suche in Google Scholar

[26] Carnegie Mellon University (CMU) Graphics Laboratory Motion Capture Database, Carnegie Mellon University, Pittsburgh, PA, USA [Online]. Available at: https://mocap.cs.cmu.edu/ [Accepted: Sep. 8, 2025].Suche in Google Scholar

[27] L Mâţiu-Iovan, “Some aspects of implementing a cubic spline interpolation algorithm on a DSP,” in 2012 10th Int. Symp. on Electronics and Telecommunications, Timisoara, Romania, IEEE, 2012, pp. 291–294.10.1109/ISETC.2012.6408096Suche in Google Scholar

[28] S. Banerjee, J. Santos, M. Hempel, and H. Sharif, “A new railyard safety approach for detection and tracking of personnel and dynamic objects using software-defined radar,” in Proc. ASME Joint Rail Conf., 2018, V001T06A014 [Online]. https://doi.org/10.1115/jrc2018-6239.Suche in Google Scholar

[29] F. Fioranelli, M. Ritchie, and H. Griffiths, “Bistatic human micro-Doppler signatures for classification of indoor activities,” in Proceedings of the 2017 IEEE Radar Conference, Seattle, WA, USA, 2017, pp. 610–615.10.1109/RADAR.2017.7944276Suche in Google Scholar

[30] Z. Zhao, H. Wang, L. Cao, D. Wang, and C. Fu, “Doppler-spread target summation variability index CFAR detector for FMCW radar,” IEEE Sens. J., vol. 24, no. 20, pp. 32519–32532, 2024, https://doi.org/10.1109/jsen.2024.3432179.Suche in Google Scholar

[31] J. Zhang, T. Jin, Y. H. L. Qiu, and Z. Zhou, “Human micro-Doppler signature extraction in the foliage-penetration environment,” in 21st International Conference on Microwave, Radar and Wireless Communications (MIKON), Krakow, Poland, IEEE, 2016, pp. 1–5.10.1109/MIKON.2016.7491979Suche in Google Scholar

[32] C. Özdemir, Inverse Synthetic Aperture Radar Imaging with MATLAB Algorithms, Second Edition, Hoboken, New Jersey, John Wiley & Sons, 2021.10.1002/9781119521396Suche in Google Scholar

[33] A. R. Persico, C. Clemente, and D. Gaglione, “On model, algorithms, and experiment for micro-doppler-based recognition of ballistic targets,” IEEE Trans. Aerosp. Electron. Syst., vol. 53, no. 3, pp. 1088–1108, 2017, https://doi.org/10.1109/taes.2017.2665258.Suche in Google Scholar

Received: 2024-07-30
Accepted: 2025-09-01
Published Online: 2025-09-15
Published in Print: 2025-12-17

© 2025 Walter de Gruyter GmbH, Berlin/Boston

Heruntergeladen am 2.1.2026 von https://www.degruyterbrill.com/document/doi/10.1515/freq-2024-0247/pdf
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