Random-Noise Denoising and Clutter Elimination of Human Respiration Movements Based on an Improved Time Window Selection Algorithm Using Wavelet Transform
| dc.contributor.author | Shikhsarmast, Farnaz Mahmoudi | |
| dc.contributor.author | Lyu, Tingting | |
| dc.contributor.author | Liang, Xiaolin | |
| dc.contributor.author | Zhang, Hao | |
| dc.contributor.author | Gulliver, Thomas Aaron | |
| dc.date.accessioned | 2019-02-07T13:45:30Z | |
| dc.date.available | 2019-02-07T13:45:30Z | |
| dc.date.copyright | 2019 | en_US |
| dc.date.issued | 2019 | |
| dc.description.abstract | This paper considers vital signs (VS) such as respiration movement detection of human subjects using an impulse ultra-wideband (UWB) through-wall radar with an improved sensing algorithm for random-noise de-noising and clutter elimination. One filter is used to improve the signal-to-noise ratio (SNR) of these VS signals. Using the wavelet packet decomposition, the standard deviation based spectral kurtosis is employed to analyze the signal characteristics to provide the distance estimate between the radar and human subject. The data size is reduced based on a defined region of interest (ROI), and this improves the system efficiency. The respiration frequency is estimated using a multiple time window selection algorithm. Experimental results are presented which illustrate the efficacy and reliability of this method. The proposed method is shown to provide better VS estimation than existing techniques in the literature. | en_US |
| dc.description.reviewstatus | Reviewed | en_US |
| dc.description.scholarlevel | Faculty | en_US |
| dc.description.sponsorship | This research was funded by the National Natural Science Foundation of China (61701462, 61501424 and 41527901), National High Technology Research and Development Program of China (2012AA061403), National Science & Technology Pillar Program during the Twelfth Five-year Plan Period (2014BAK12B00), Ao Shan Science and Technology Innovation Project of Qingdao National Laboratory for Marine Science and Technology (2017ASKJ01), Qingdao Science and Technology Plan (17-1-1-7-jch), and the Fundamental Research Funds for the Central Universities (201713018). | en_US |
| dc.identifier.citation | Shikhsarmast, F.M., Lyu, T., Liang, X., Zhang, H. & Gulliver, T.A. (2019). An Improved Unauthorized Unmanned Aerial Vehicle Detection Algorithm Using Radiofrequency-Based Statistical Fingerprint Analysis. Sensors, 19(1), 95. https://doi.org/10.3390/s19010095 | en_US |
| dc.identifier.uri | http://dx.doi.org/10.3390/s19010095 | |
| dc.identifier.uri | http://hdl.handle.net/1828/10596 | |
| dc.language.iso | en | en_US |
| dc.publisher | Sensors | en_US |
| dc.subject | vital sign | |
| dc.subject | ultra-wideband impulse radar | |
| dc.subject | wavelet packet decomposition | |
| dc.subject.department | Department of Electrical and Computer Engineering | |
| dc.title | Random-Noise Denoising and Clutter Elimination of Human Respiration Movements Based on an Improved Time Window Selection Algorithm Using Wavelet Transform | en_US |
| dc.type | Article | en_US |