Short-Range Vital Signs Sensing Based on EEMD and CWT Using IR-UWB Radar

Hu, Xiaoming; Tian, Jie

doi:10.20944/preprints201608.0206.v1

Cited by 13 publications

(6 citation statements)

References 14 publications

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“…Meanwhile, the high-order cumulants of reconstructed signals were calculated to improve measurement accuracy [16]. In [17], EEMD was used to improve the signal-to-noise ratio of echo signals. Continuous wavelet transforms (CWT) was used to separate the heartbeat and respiratory signals from radar-received signals, but the performance of the algorithm depended on the selection of the wavelet basis.…”

Section: Introductionmentioning

confidence: 99%

Vital Sign Signal Extraction Method Based on Permutation Entropy and EEMD Algorithm for Ultra-Wideband Radar

2019

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The noncontact measurement of vital sign signals is useful for medical care, rescuing disaster survivors from ruins and public safety. In this paper, a novel vital sign signal extraction method based on permutation entropy (PE) and ensemble empirical mode decomposition (EEMD) algorithm is proposed. The proposed algorithm analyzes the permutation entropy of radar-received pulses; the range between a human target and ultra-wideband (UWB) radar can be obtained by permutation entropy. Permutation entropy represents the complexity of signals, so we can use PE to select and recombine human life signals that are distributed in the adjacent distance gate. Moreover, EEMD algorithm is adopted to decompose the combined signal into intrinsic mode functions (IMF), and both the respiration and the heartbeat signals are reconstructed by IMF via reaching the energy threshold in the time domain. Experiments are carried out using UWB radar. Compared with traditional algorithms, the proposed algorithm can be used to extract the range and frequency information of human targets efficiently and accurately. INDEX TERMSVital sign signal, ultra-wideband (UWB) radar, ensemble empirical mode decomposition (EEMD), permutation entropy (PE).

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Section: Introductionmentioning

confidence: 99%

Vital Sign Signal Extraction Method Based on Permutation Entropy and EEMD Algorithm for Ultra-Wideband Radar

2019

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“…Conventional studies on UWB radars are considered to suppress various clutters, estimating parameters to analyze signal characteristics, and other related problems. To suppress clutter, the classic ensemble empirical mode decomposition (EEMD) technique was used in a previous study [12] to estimate target position by improving the signal-to-noise ratio (SNR) and removing clutter. However, most detection techniques for UWB radars are ineffective at obtaining accurate direction of arrival (DOA) because the two or more UWB radars are needed to estimate DOA.…”

Section: Introductionmentioning

confidence: 99%

Extrapolation-RELAX Estimator Based on Spectrum Partitioning for DOA Estimation of FMCW Radar

Kim

Jin

et al. 2019

IEEE Access

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This paper proposes an extrapolation-RELAX estimator based on spectrum partitioning (SP) for the direction of arrival (DOA) estimation of frequency-modulated continuous-wave (FMCW) radar. The FMCW radar employs fast Fourier transform (FFT)-based digital beamforming (DBF) for the DOA estimation owing to its low complexity and easy implementation. However, the DBF algorithm has a disadvantage of low angle resolution. To improve the angle resolution, super-resolution algorithms such as multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariance techniques (ESPRIT) are proposed. However, these algorithms require the high signal-to-noise ratio (SNR) to meet the required performance. To overcome this drawback of super-resolution algorithms, the SP-based extrapolation method has been proposed. However, this algorithm still has the problem that the resolution performance degrades owing to the insufficient number of actual antenna arrays. To solve this problem, we propose the SP-based extrapolation-RELAX algorithm for DOA estimation of FMCW radar. Through extrapolation, the proposed structure solves the problem of insufficient number of arrays, resulting in high reliability of SP results. When the extrapolation algorithm is used to generate the input signal of the RELAX algorithm, the RELAX method improves the performance of the DOA estimation. To confirm the effectiveness of the proposed estimation, we compare the Monte Carlo simulation and root-mean-square error results of the proposed and conventional algorithms. To verify the performance of the proposed algorithm in practical conditions, experiments were performed using the FMCW radar module within a chamber and in an indoor environment.

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“…Many algorithms for life sign detection have been proposed recently [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42]. L. Liu analysed the time-frequency characteristic of human respiratory by employing the Hilbert transform based Fourier transform [23][24].…”

Section: Introductionmentioning

confidence: 99%

“…Y. Xu analyzed the results after suppressing Gaussian noise using the fourth order cumulant method [37]. X. Hu discussed human heartbeat signals via extracting life signs based on the intrinsic mode function [38]. B. K. Park performed the AD algorithm on improving the accuracy of heartbeat rate [40].…”

Section: Introductionmentioning

confidence: 99%

Detection of Breathing Rates in Through-wall UWB Radar Utilizing JTFA

2019

KSII TIIS

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Through-wall ultra-wide band (UWB) radar has been considered as one of the preferred and non-contact technologies for the targets detection owing to the better time resolution and stronger penetration. The high time resolution is a result of a larger of bandwidth of the employed UWB pulses from the radar system, which is a useful tool to separate multiple targets in complex environment. The article emphasised on human subject localization and detection. Human subject usually can be detected via extracting the weak respiratory signals of human subjects remotely. Meanwhile, the range between the detection object and radar is also acquired from the 2D range-frequency matrix. However, it is a challenging task to extract human respiratory signals owing to the low signal to clutter ratio. To improve the feasibility of human respiratory signals detection, a new method is developed via analysing the standard deviation based kurtosis of the collected pulses, which are modulated by human respiratory movements in slow time. The range between radar and the detection target is estimated using joint time-frequency analysis (JTFA) of the analysed characteristics, which provides a novel preliminary signature for life detection. The breathing rates are obtained using the proposed accumulation method in time and frequency domain, respectively. The proposed method is validated and proved numerically and experimentally.

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Short-Range Vital Signs Sensing Based on EEMD and CWT Using IR-UWB Radar

Cited by 13 publications

References 14 publications

Vital Sign Signal Extraction Method Based on Permutation Entropy and EEMD Algorithm for Ultra-Wideband Radar

Vital Sign Signal Extraction Method Based on Permutation Entropy and EEMD Algorithm for Ultra-Wideband Radar

Extrapolation-RELAX Estimator Based on Spectrum Partitioning for DOA Estimation of FMCW Radar

Detection of Breathing Rates in Through-wall UWB Radar Utilizing JTFA

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