Abstract
Ultra-wideband (UWB) impulse radar plays an important role in contactless vital sign (VS) detection. The VS can be extracted remotely by acquiring the oscillations in the human chest. Unfortunately, it is usually challenging to identify VS due to the low signal-to-noise ratio (SNR) only based on the traditional fast Fourier transform (FFT) especially in complicated conditions. To extract VS accurately, this paper presents a new scheme by analyzing the skewness characteristic of the received UWB impulses, which are modulated by life activities. The distance from the human subject to the radar antenna can be calculated by performing the discrete short-time Fourier transform (DSFT) on skewness. The frequency of human respiratory movement can be estimated based on the developed ensemble empirical mode decomposition (EEMD)-based accumulation technique by canceling out the harmonics effectively. The performance of the developed detection method is tested with several experiments carried out in different environments.
Highlights
Contactless vital sign (VS) detection has drawn wide attention and achieved great achievements [1,2,3,4].The electromagnetic detection is regarded as the most promising technique, which can acquire VS in the range-frequency matrix
The used UWB radar is placed on a table, which is 1.5 m in the air
We can see that the developed method shows better capability of improving signal-to-noise ratio (SNR) than the constant false alarm ratio (CFAR) method such as SNR is 5.07 dB using the new method while it is − 5.21 dB for the CFAR method at 7 m
Summary
Contactless vital sign (VS) detection has drawn wide attention and achieved great achievements [1,2,3,4].The electromagnetic detection is regarded as the most promising technique, which can acquire VS in the range-frequency matrix. Ultra-wide band (UWB) impulse radar has been widely applied in indoor target localization, VS detection, and human fall detection by employing the continuous wave [5,6,7,8]. UWB pulse radar has been widely used in moving target detection [3, 9, 10], through-wall imaging [11, 12] and post-earthquake search and rescue [13,14,15] due to its insightful advantages such as strong permeability and excellent time resolution. Considering the additive white Gaussian noise (AWGN), a maximum likelihood period estimator with lower
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More From: EURASIP Journal on Wireless Communications and Networking
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