Accurate Doppler Radar Noncontact Vital Sign Detection Using the RELAX Algorithm

Li, Changzhi; Ling, Jun; Li, Jian; Lin, Jenshan

doi:10.1109/tim.2009.2025986

Cited by 138 publications

(22 citation statements)

References 16 publications

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“…Solving the problem in Eq. (14) usually leads to an NPhard problem that becomes computationally intractable even for moderate dimensions. One breakthrough came with the proof that, for enough sparse signals and sensing matrices with sufficiently incoherent columns, the l 0 -norm minimization is equivalent to the l 1 -norm minimization, 26,27 which is the closest convex optimization problem to Eq.…”

Section: A Sparse Reconstructionmentioning

confidence: 99%

“…One breakthrough came with the proof that, for enough sparse signals and sensing matrices with sufficiently incoherent columns, the l 0 -norm minimization is equivalent to the l 1 -norm minimization, 26,27 which is the closest convex optimization problem to Eq. (14) and can be solved in a polynomial time. Another branch is greedy pursuits, which is based on the simple concept of finding the atom that has the largest correlation with the measurement, eliminating it from the data and repeating with the rest until convergence.…”

Section: A Sparse Reconstructionmentioning

confidence: 99%

“…In 2010, a parametric and cyclic optimization algorithm (RELAX 13 ) was adopted by Li for non-contact vital sign measurement. 14 Simulation results showed that it could separate the heartbeat component from the third-order harmonic of respiration with a frequency bias of 3 beats/min within a processing window length of 12.015 s. However, the choice of the parameter which indicates how many sinusoids should be taken into consideration is difficult to evaluate. In 2013, the multiple signal classification (MUSIC) 15 parametric spectral estimation algorithm was applied into this field.…”

Section: Introductionmentioning

confidence: 99%

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Super-resolution spectral estimation in short-time non-contact vital sign measurement

Sun

Hong

et al. 2015

Review of Scientific Instruments

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Non-contact techniques for measuring vital signs attract great interest due to the benefits shown in medical monitoring, military application, etc. However, the presence of respiration harmonics caused by nonlinear phase modulation will result in performance degradation. Suffering from smearing and leakage problems, conventional discrete Fourier transform (DFT) based methods cannot distinguish the heartbeat component from closely located respiration harmonics in frequency domain, especially in short-time processing. In this paper, the theory of sparse reconstruction is merged with an extended harmonic model of vital signals, aiming at achieving a super-resolution spectral estimation of vital signals by additionally exploiting the inherent sparse prior information. Both simulated and experimental results show that the proposed algorithm has superior performance to DFT-based methods and the recently applied multiple signal classification algorithm, and the required processing window length has been shortened to 5.12 s.

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Section: A Sparse Reconstructionmentioning

confidence: 99%

Section: A Sparse Reconstructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Super-resolution spectral estimation in short-time non-contact vital sign measurement

Sun

Hong

et al. 2015

Review of Scientific Instruments

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“…The use of electromagnetic wave propagation principles for developing non-contacting sensors has been proposed in previous researches. Doppler radar was studied and proposed for detecting vital sign, for examples respiratory and heartbeat [1][2][3][4][5]. In order to monitor the condition of civil structures, the use of radar systems as tools for measuring the structure displacement and monitoring the vibration characteristic was evaluated [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…This high range resolution leads to a large required bandwidth [11][12][13]. In order to mitigate this issue, some previous works proposed the use of phase data processing as a basic detection principle to avoid the need of large bandwidth for high range resolution [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Non-Contacting Sensor for Small Displacement and Vibration Monitoring Based on Reflection Coefficient Measurement

Pramudita¹,

Ariananda²,

Edwar³

2018

PIER M

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Abstract-In some cases, such as at a boiler tank and other large-size mechanical systems, it is more realistic to employ a non-contacting sensor to detect small displacement or vibration. In this paper, a non-contacting sensor for monitoring small displacement or vibration based on measurement of antenna reflection coefficient is proposed. A theoretical and numerical study is performed to investigate the proposed method and to determine the post processing method associated with the antenna reflection coefficient data. To avoid the ambiguity in the measured data, the detection of both the magnitude and phase components of the antenna reflection coefficient is required to compute the small displacement of the target. The distance between antenna and target has to be determined in order to minimize the ambiguity range in the data. The frequency domain observation is more appropriate for determining the amplitude and frequency of the target vibration. Magnitude detection, phase detection and Fourier analysis are used as main tools in the post-processing part of the proposed method.

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Detection of moving human micro‐Doppler signature in forest environments with swaying tree components by wind

et al. 2015

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The objective of this paper is to investigate human motion in forest medium with swaying tree components due to time-varying wind effects and to observe the characteristics of the received Doppler signature from the scene. We provide the results of an accurate model accounting for the key contributions to the Doppler signature in this scenario. A realistic walking motion is generated using an analytical model extracted from empirical data. The swaying canopy motion is modeled by employing a spring response mechanism to the wind force. The backscattered field calculations from the scene comprise of contributions from the forest (including trunks, branches, and the ground) and human, and the interactions between them. An analytical forest scattering model, which accounts for the ground effects, is used to calculate the contribution from the forest. The attenuation effects due to the vegetation are accounted for. In order to characterize the effects of human motion accurately, a full wave technique, namely, method of moments (MOM) enhanced with fast multipole method (FMM), is employed for the human scattering calculations. A parallel version of MOM-FMM is implemented on a graphics processing unit based cluster to handle the large problem size. The human walking signatures created by the model are analyzed for different winds.

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Accurate Doppler Radar Noncontact Vital Sign Detection Using the RELAX Algorithm

Cited by 138 publications

References 16 publications

Super-resolution spectral estimation in short-time non-contact vital sign measurement

Super-resolution spectral estimation in short-time non-contact vital sign measurement

Non-Contacting Sensor for Small Displacement and Vibration Monitoring Based on Reflection Coefficient Measurement

Detection of moving human micro‐Doppler signature in forest environments with swaying tree components by wind

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