2604003727
  • Open Access
  • Article

Harmonic-Suppressed BR/HR Detection in FMCW Radar via Phase Correction with APSVD and VMD-Denoising

  • Yukun Huang 1,†,   
  • Denghao Li 1,†,   
  • Jingran Cheng 1,   
  • Haoming Feng 1,   
  • Min Duan 2,   
  • Junlin Yu 3,   
  • Jinqiu Zhou 3,   
  • Shengwei Liu 3,   
  • Zheng Wang 1,   
  • Yonghui Wu 1,   
  • Huaqing Li 1,*

Received: 12 Jan 2026 | Revised: 18 Apr 2026 | Accepted: 22 Apr 2026 | Published: 07 Jul 2026

Abstract

Radar has been used more and more in healthcare monitoring, so non-contact vital sign detection has received much attention. In real applications, it is still hard to separate heartbeat and respiration signals well because noise and respiratory harmonics often mix with weak heartbeat components. In this work, a vital signal enhancement and decomposition method for FMCW radar is presented. The method combines phase correction with a signal decomposition scheme based on adaptive projection singular value decomposition (APSVD) and variational mode decomposition (VMD) for denoising. First, chest motion is recovered from the phase of echo signals, and the phase-correction process improves signal quality. Then, APSVD is used to extract the respiration signal and suppress its harmonics. After that, VMD-based denoising is applied to the remaining signal so that the heartbeat signal can be separated more clearly. The experimental results show that the proposed method achieves an RMSE of 1.550827 bpm for breath rate (BR) estimation. For heart rate (HR) estimation, it obtains the lowest MAE and MRE among the compared methods, demonstrating improved average estimation accuracy while maintaining competitive RMSE performance.

References 

  • 1.

    Al-Mahmud, O.; Khan, K.; Roy, R.; et al. Internet of Things (IoT) Based Smart Health Care Medical Box for Elderly People. In Proceedings of the 2020 International Conference for Emerging Technology (INCET), Belgaum, India, 5–7 June 2020.

  • 2.

    Liao, C.; Shay, O.; Gomes, E.; et al. Noninvasive Continuous Blood Pressure Measurement with Wearable Millimeter Wave Device. In Proceedings of the 2021 IEEE 17th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Athens, Greece, 27–30 July 2021. https://doi.org/10.1109/BSN51625.2021.9507020.

  • 3.

    Yang, X.; Fan, D.; Ren, A.; et al. Sleep Apnea Syndrome Sensing at C-Band. IEEE J. Transl. Eng. Health Med. 2018, 6, 2701008. https://doi.org/10.1109/JTEHM.2018.2879085.

  • 4.

    Zhao, X.; Wang, W.; Li, C.; et al. Diagnosis of Sleep Apnea Hypopnea Syndrome Using Fusion of Micro-Motion Signals from Millimeter-Wave Radar and Pulse Wave Data. J. Radars 2025, 14, 102–116. https://doi.org/10.12000/JR24107.

  • 5.

    Gianoglio, C.; Mohanna, A.; Rizik, A.; et al. On Edge Human Action Recognition Using Radar-Based Sensing and Deep Learning. IEEE Trans. Ind. Inform. 2024, 20, 4160–4172.

  • 6.

    Thornburg, A.; Bai, T.; Heath, R.W. Performance Analysis of Outdoor mmWave Ad Hoc Networks. IEEE Trans. Signal Process. 2016, 64, 4065–4079.

  • 7.

    Dai, T.K.V.; Oleksak, K.; Kvelashvili, T.; et al. Enhancement of Remote Vital Sign Monitoring Detection Accuracy Using Multiple-Input Multiple-Output 77 GHz FMCW Radar. IEEE J. Electromagn. Microwaves Med. Biol. 2022, 6, 111–122. https://doi.org/10.1109/JERM.2021.3082807.

  • 8.

    Li, C.; Lubecke, V.; Boric-Lubecke, O.; et al. Sensing of Life Activities at the Human-Microwave Frontier. IEEE J. Microwaves 2021, 1, 66–78.

  • 9.

    Ma, L.; Wang, Z.; Fan, J.; et al. Interpretation of Report on Cardiovascular Health and Diseases in China 2022. Chin. Gen. Pract. 2023, 26, 3975–3994. (In Chinese)

  • 10.

    Xu, C.; Hu, N.; Li, Y. A Millimeter Wave Radar Target Detection Method in K-Distributed Clutter Background. In Proceedings of the 2021 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS), Shenyang, China, 10–11 December 2021; pp. 489–493. https://doi.org/10.1109/TOCS53301.2021.9688689.

  • 11.

    Liu, J.; Li, Y.; Li, C.; et al. Accurate Measurement of Human Vital Signs with Linear FMCW Radars under Proximity Stationary Clutters. IEEE Trans. Biomed. Circuits Syst. 2021, 15, 1393–1404. https://doi.org/10.1109/TBCAS.2021.3123830.

  • 12.

    Wang, G.;Munoz-Ferreras, J.-M.; Gu, C.; et al. Application of Linear-Frequency-Modulated Continuous-Wave (LFMCW) Radars for Tracking of Vital Signs. IEEE Trans. Microw. Theory Tech. 2014, 62, 1387–1399. https://doi.org/10.1109/TMTT.2014.2320464.

  • 13.

    Ayhan, S.; Scherr, S.; Bhutani, A.; et al. Impact of Frequency Ramp Nonlinearity, Phase Noise, and SNR on FMCW Radar Accuracy. IEEE Trans. Microw. Theory Tech. 2016, 64, 3290–3301. https://doi.org/10.1109/TMTT.2016.2599165.

  • 14.

    Paterniani, G.; Sgreccia, D.; Davoli, A.; et al. Radar-Based Monitoring of Vital Signs: A Tutorial Overview. Proc. IEEE 2023, 111, 277–317. https://doi.org/10.1109/JPROC.2023.3244362.

  • 15.

    Li, C.; Peng, Z.; Huang, T.; et al. A Review on Recent Progress of Portable Short-Range Noncontact Microwave Radar Systems. IEEE Trans. Microw. Theory Tech. 2017, 65, 1692–1706. https://doi.org/10.1109/TMTT.2017.2650911.

  • 16.

    Ni, Z.; Huang, B. Open-Set Human Identification Based on Gait Radar Micro-Doppler Signatures. IEEE Sensors J. 2021, 21, 8226–8233. https://doi.org/10.1109/JSEN.2021.3052613.

  • 17.

    Dang, X.; Zhang, J.; Hao, Z. A Non-Contact Detection Method for Multi-Person Vital Signs Based on IR-UWB Radar. Sensors 2022, 22, 6116.

  • 18.

    Sadreazami, H.; Bolic, M.; Rajan, S. Fall Detection Using Standoff Radar-Based Sensing and Deep Convolutional Neural Network. IEEE Trans. Circuits Syst. II Express Briefs 2020, 67, 197–201. https://doi.org/10.1109/TCSII.2019.2904498.

  • 19.

    Zhang, F.; Wang, Z.; Jin, B.; et al. Your Smart Speaker Can “Hear” Your Heartbeat! Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2020, 4, 1–24. https://doi.org/10.1145/3432237.

  • 20.

    Xu, Z.; Shi, C.; Zhang, T.; et al. Simultaneous Monitoring of Multiple People’s Vital Sign Leveraging a Single Phased-MIMO Radar. IEEE J. Electromagn. Microwaves Med. Biol. 2022, 6, 311–320. https://doi.org/10.1109/JERM.2022.3143431.

  • 21.

    Fang, Z.; Jian, P.; Zhang, H.; et al. Review of Noncontact Medical and Health Monitoring Technologies Based on FMCW Radar. J. Radars 2022, 11, 499–516. https://doi.org/10.12000/JR22019 (In Chinese)

  • 22.

    Bae, C.; Lee, S.; Jung, Y. High-Speed Continuous Wavelet Transform Processor for Vital Signal Measurement Using Frequency-Modulated Continuous Wave Radar. Sensors 2022, 22, 3073.

  • 23.

    Seena, V.; Yomas, J. A Review on Feature Extraction and Denoising of ECG Signal UsingWavelet Transform. In Proceedings of the 2nd International Conference on Devices, Circuits and Systems (ICDCS), Coimbatore, India, 6–8 March 2014. https://doi.org/10.1109/ICDCSyst.2014.6926190.

  • 24.

    Wu, Y.; Ni, H.; Mao, C.; et al. Contactless Reconstruction of ECG and Respiration Signals with mmWave Radar Based on RSSRnet. IEEE Sensors J. 2024, 24, 6358–6368. https://doi.org/10.1109/JSEN.2023.3333025.

  • 25.

    Wang, H.; Du, F.; Zhu, H.; et al. HeRe: Heartbeat Signal Reconstruction for Low-Power Millimeter-Wave Radar Based on Deep Learning. IEEE Trans. Instrum. Meas. 2023, 72, 4004515. https://doi.org/10.1109/TIM.2023.3267348.

  • 26.

    Bauder, C.; Moadi, A.-K.; Rajagopal, V.; et al. mm-MuRe: mmWave-Based Multi-Subject Respiration Monitoring via End-to-End Deep Learning. IEEE J. Electromagn. Microwaves Med. Biol. 2025, 9, 49–61. https://doi.org/10.1109/JERM.2024.3443782.

  • 27.

    Strodthoff, N.; Wagner, P.; Schaeffter, T.; et al. Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL. IEEE J. Biomed. Health Inform. 2021, 25, 1519–1528. https://doi.org/10.1109/JBHI.2020.3022989.

  • 28.

    Xu, D.; Yu, W.; Deng, C.; et al. Non-Contact Detection of Vital Signs Based on Improved Adaptive EEMD Algorithm. Sensors 2022, 22, 6423.

  • 29.

    Yan, J.; Hong, H.; Zhao, H.; et al. Through-Wall Multiple Targets Vital Signs Tracking Based on VMD Algorithm. Sensors 2016, 16, 1293. https://doi.org/10.3390/s16081293.

  • 30.

    Wang, F.; Zeng, X.; Wu, C.; et al. mmHRV: Contactless Heart Rate Variability Monitoring Using Millimeter-Wave Radio. IEEE Internet Things J. 2021, 8, 16623–16636. https://doi.org/10.1109/JIOT.2021.3075167.

  • 31.

    Wu, Z.; Huang, N. Ensemble Empirical Mode Decomposition: A Noise-Assisted Data Analysis Method. Adv. Data Sci. Adapt. Anal. 2009, 1, 1–41. https://doi.org/10.1142/S1793536909000047.

  • 32.

    Torres, M.; Colominas, M.; Schlotthauer, G.; et al. A Complete Ensemble Empirical Mode Decomposition with Adaptive Noise. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic, 22–27 May 2011; pp. 4144–4147. https://doi.org/10.1109/ICASSP.2011.5947265.

  • 33.

    Dragomiretskiy, K.; Zosso, D. Variational Mode Decomposition. IEEE Trans. Signal Process. 2014, 62, 531–544. https://doi.org/10.1109/TSP.2013.2288675.

  • 34.

    Wang, Y.; Markert, R.; Xiang, J.; et al. Research on Variational Mode Decomposition and Its Application in Detecting Rub-Impact Fault of the Rotor System. Mech. Syst. Signal Process. 2015, 60–61, 243–251. https://doi.org/10.1016/j.ymssp.2015.02.020.

  • 35.

    Ahmad, A.; Roh, J.; Wang, D.; et al. Vital Signs Monitoring of Multiple People Using a FMCW Millimeter-Wave Sensor. In Proceedings of the IEEE Radar Conference (RadarConf18), Oklahoma City, OK, USA, 23–27 April 2018; pp. 1450–1455. https://doi.org/10.1109/RADAR.2018.8378778.

  • 36.

    IWR1642 BoosterPack Evaluation Module for Single-Chip 77GHz mmWave Sensor. Available online: https://www.ti.com/tool/IWR1642BOOST (accessed on 19 May 2020) .

  • 37.

    DCA1000 Evaluation Module for Real-Time Data Capture and Streaming. Available online: https://www.ti.com/tool/DCA1000EVM (accessed on 1 February 2019).

  • 38.

    Kang, D.; Ming, X.; Xiaofei, Z. Phase Difference Correction Method for Phase and Frequency in Spectral Analysis. Mech. Syst. Signal Process. 1999, 14, 835–843.

  • 39.

    Qu, L.; Liu, C.; Yang, T.; et al. Vital Sign Detection of FMCW Radar Based on Improved Adaptive Parameter Variational Mode Decomposition. IEEE Sensors J. 2023, 23, 25048–25060. https://doi.org/10.1109/JSEN.2023.3312513.

  • 40.

    Bhowmik, S.; Jelfs, B.; Arjunan, S.P.; et al. Outlier Removal in Facial Surface Electromyography through Hampel Filtering Technique. In Proceedings of the IEEE Life Sciences Conference (LSC), Sydney, NSW, Australia, 13–15 December 2017; pp. 258–261. https://doi.org/10.1109/LSC.2017.8268192.

  • 41.

    Wall, M.E.; Rechtsteiner, A.; Rocha, L.M. Singular Value Decomposition and Principal Component Analysis. In A Practical Approach to Microarray Data Analysis; Berrar, D.P., Dubitzky, W., Granzow, M., Eds.; Springer: Boston, MA, USA, 2003; pp. 91–109.

  • 42.

    American Heart Association. All About Heart Rate (Pulse). Available online: https://www.heart.org/en/health-topics/highblood-pressure/the-facts-about-high-blood-pressure/all-about-heart-rate-pulse (accessed on 13 May 2024).

Share this article:
How to Cite
Huang, Y.; Li, D.; Cheng, J.; Feng, H.; Duan, M.; Yu, J.; Zhou, J.; Liu, S.; Wang, Z.; Wu, Y.; Li, H. Harmonic-Suppressed BR/HR Detection in FMCW Radar via Phase Correction with APSVD and VMD-Denoising. Journal of Machine Learning and Information Security 2026, 2 (3), 14. https://doi.org/10.53941/jmlis.2026.100014.
RIS
BibTex
Copyright & License
article copyright Image
Copyright (c) 2026 by the authors.