Clutter Elimination and Harmonic Suppression of Non-Stationary Life Signs for Long-Range and Through-Wall Human Subject Detection Using Spectral Kurtosis Analysis (SKA)-Based Windowed Fourier Transform (WFT) Method

Life sign detection is important in many applications, such as locating disaster victims. This can be difficult in low signal to noise ratio (SNR) and through-wall conditions. This paper considers life sign detection using an impulse ultra-wideband (UWB) bio-radar with an improved sensing algorithm...

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Bibliographic Details
Published inApplied sciences Vol. 9; no. 2; p. 355
Main Authors Yang, Shengying, Qin, Huibin, Liang, Xiaolin, Gulliver, Thomas
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 21.01.2019
Subjects
Algorithms
Clutter
Data acquisition
Decomposition
Fourier analysis
Fourier transforms
frequency accumulator
Heart rate
Human motion
Human subjects
Kurtosis
Noise
Propagation
Radar systems
Receivers & amplifiers
Respiration
Sampling
ultra-wideband (UWB) bio-radar
victim detection
windowed Fourier transform (WFT)
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Summary:Life sign detection is important in many applications, such as locating disaster victims. This can be difficult in low signal to noise ratio (SNR) and through-wall conditions. This paper considers life sign detection using an impulse ultra-wideband (UWB) bio-radar with an improved sensing algorithm for clutter elimination, harmonic suppression and random-noise de-noising. To improve detection performance, two filters are used to improve SNR of these life signs. The automatic gain method is performed in fast time to improve the respiration signals. The spectral kurtosis analysis (SKA)-based windowed Fourier transform (WFT) method and an accumulator in the frequency domain are used to provide two distance estimates between the radar and human subject. Further, the accumulator can also provide the frequency estimate of the respiration signals. These estimates are used to determine if a human is present in the detection environment. Results are presented which show that the range and respiration frequency can be estimated accurately in low signal to noise and clutter ratio (SNCR) environments. In addition, the performance is better than with other techniques given in the literature.
ISSN:2076-3417
2076-3417
DOI:10.3390/app9020355