Enhance micro-Doppler signatures-based human activity classification accuracy of FMCW radar using the threshold method



  • Nguyen Ngoc Binh Faculty of Radio-Electronics Engineering, Le Quy Don Technical University
  • Pham Minh Nghia (Corresponding Author) Faculty of Radio-Electronics Engineering, Le Quy Don Technical University
  • Phan Huy Anh Academy of Military Science and Technology
  • Pham Hoang Hung Institute of Simulation Technology, Le Quy Don Technical University




Micro-Doppler signature; Convolutional Neural Network; Human Activity Classification.


Nowadays, radar-based human activity classification is being widely adopted in healthcare systems due to its benefits in terms of personal privacy compliance, non-contact sensing, and being unaffected by weather conditions. This study proposes a threshold method in the pre-processing stage to improve human activity classification accuracy by determining the region of meaningful information (RMI) on the spectrogram. Initially, a mask function, which is created by a certain threshold value, is applied to the input spectrogram to highlight the RMI from the micro-Doppler (m-D) signatures. Only the highlighted RMI on the spectrogram is retained as input to the classifiers. Then, five Convolutional Neural Networks (CNNs) of varying complexity are employed to extract features, identify activities, and assess the effectiveness of the suggested approach. The experimental results demonstrate that the suggested approach has enhanced classification accuracy by up to 11% when compared to the original unprocessed dataset.


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How to Cite

Nguyen, . N. B., A. N. Pham Minh, A. Phan Huy, and Pham Hoang Hung. “Enhance Micro-Doppler Signatures-Based Human Activity Classification Accuracy of FMCW Radar Using the Threshold Method”. Journal of Military Science and Technology, vol. 95, no. 95, May 2024, pp. 20-28, doi:10.54939/1859-1043.j.mst.95.2024.20-28.



Research Articles