卷积神经网络
人工智能
计算机科学
雷达
基带
模式识别(心理学)
语音识别
计算机视觉
电信
带宽(计算)
作者
Homa Arab,Iman Ghaffari,Lydia Chioukh,Serioja Ovidiu Tatu,Steven Dufour
出处
期刊:IEEE Sensors Journal
[Institute of Electrical and Electronics Engineers]
日期:2022-01-06
卷期号:22 (5): 4494-4502
被引量:16
标识
DOI:10.1109/jsen.2022.3140787
摘要
Human movement detection based on millimeter-wave radar sensors is a technology of interest in various areas such as for smart surveillance, security, behavioral biometrics, biomedical systems, robotics, etc. This paper shows the feasibility and effectiveness of using a compact 24 GHz Doppler radar with a built-in low-noise microwave amplifier (LNA) for detecting and extracting signals coming from human motion. Reliability and accuracy is assessed based on a comprehensive theoretical analysis, and on simulation results followed by experimental investigations. A continuous wavelet transform (CWT) is used to decompose in-phase and quadrature ( ${I/Q}$ ) baseband signals to extract information about the dynamics of the system. The dataset consists of 1000 recordings in 8 motion classes (standing, walking, sitting, etc.). We propose to apply a two-channel convolutional neural network (CNN), which is composed of two CNN channels, for learning high-level features from time-domain signals and CWT spectrograms. One channel has four one-dimensional (1D) convolutional and pooling layers, and the other channel is made of three two-dimensional (2D) convolutional and pooling layers. In addition, the ${I/Q}$ signals are denoised by using Savitzky-Golay filtering, and both noisy and denoised signals are used as input signals for deep learning data augmentation purpose. We achieved an overall classification accuracy rate of 98.85% in motion classification for a two-branch CNN architecture, and an accuracy rate of 95.3% for a one-branch 2D-CNN. Our results show that a dual-channel CNN model can greatly increase the classification capabilities of human motion recognition and classification, and the proposed method can be effectively used with various radar signal classifications.
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