Improving human activity classification based on micro-doppler signatures of FMCW radar with the effect of noise.

Autor: Nguyen N; Faculty of Radio Electronics Engineering, Le Quy Don Technical University, Hanoi, Vietnam., Pham M; Faculty of Radio Electronics Engineering, Le Quy Don Technical University, Hanoi, Vietnam., Doan VS; VietNam Naval Academy, Nha Trang, Khanh Hoa, Vietnam., Le V; Faculty of Radio Electronics Engineering, Le Quy Don Technical University, Hanoi, Vietnam.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2024 Aug 01; Vol. 19 (8), pp. e0308045. Date of Electronic Publication: 2024 Aug 01 (Print Publication: 2024).
DOI: 10.1371/journal.pone.0308045
Abstrakt: Nowadays, classifying human activities is applied in many essential fields, such as healthcare, security monitoring, and search and rescue missions. Radar sensor-based human activity classification is regarded as a superior approach in comparison to other techniques, such as visual perception-based methodologies and wearable gadgets. However, noise usually exists throughout the process of extracting raw radar signals, decreasing the quality and reliability of the extracted features. This paper presents a novel method for removing white Gaussian noise from raw radar signals using a denoising algorithm before classifying human activities using a deep convolutional neural network (DCNN). Specifically, the denoising algorithm is used as a preprocessing step to remove white Gaussian noise from the input raw radar signal. After that, a lightweight Cross-Residual Convolutional Neural Network (CRCNN) with adaptable cross-residual connections is suggested for classification. The analysis results show that the denoising algorithm with a range-bin interval of 3 and a cut-threshold value of 3 achieves the best denoising effect. When the denoising algorithm was applied to the dataset, CRCNN improved the right classification rate by up to 10% compared to the recognition results achieved with the original noise-added dataset. Additionally, a comparison of the CRCNN with the denoising algorithm solution with six cutting-edge DCNNs was conducted. The experimental results reveal that the proposed model greatly outperforms the others.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Nguyen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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