Applications of TVF‐EMD in Vital Signal Detection for UWB Radar

Yang, Zhen; Chi, Ma; Qi, Qingjie; Li, Xin; Li, Yan

doi:10.1155/2021/2136614

Cited by 9 publications

(3 citation statements)

References 34 publications

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“…(1) Applying peak detection or data clustering to the processing results of signal energy [20,21], standard deviation [22,23], variance [24], peak-to-peak value [24], correlation [25,26], and even entropy [27] from the obtained data matrix;…”

Section: Introductionmentioning

confidence: 99%

Two-Step Accuracy Improvement for Multitarget Detection in Complex Environment Using UWB Radar

Liang,

Jin,

Yang

et al. 2024

Remote Sensing

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Detecting multiple human targets in indoor scenarios using ultra-wideband (UWB) radar usually involves false detection results caused by the secondary reflections, which might reduce the target detection accuracy and cause a more severe deterioration when the number of targets increases. This article proposed a two-step accuracy improvement method for multitarget detection in environments with multiple human targets of more than three and strong secondary reflections by the surroundings, especially the walls. Based on the rough detection results acquired by the modified CA-CFAR (MCA-CFAR) processing, the first step achieves the primary false alarm suppression using a short-window accumulation in the time domain. Then, the second step applies the decision confidence on the detection results from the first step to assess the reliability of results for improved accuracy. The two-step accuracy improvement could thus have a higher accuracy through cascading false alarm suppression. The effectiveness and accuracy of the proposed algorithm are verified based on the experimental results.

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Section: Introductionmentioning

confidence: 99%

Two-Step Accuracy Improvement for Multitarget Detection in Complex Environment Using UWB Radar

Liang,

Jin,

Yang

et al. 2024

Remote Sensing

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show abstract

“…It has the advantages of strong penetration, high resolution, good anti-jamming etc. [7][8][9][10]. It emits nanosecond pulses of electromagnetic waves over long distances, based on the time-domain Doppler effect generated by human motion on radar echoes, to analyze the presence of human targets in the detection range and the specific location and vital signs of each target [11,12].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Target Localization and Vital Sign Detection Method Using Ultra-Wide Band Radar

Zhang

Cheng

et al. 2023

Sensors

Self Cite

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Life detection technology using ultra-wideband (UWB) radar is a non-contact, active detection technology, which can be used to search for survivors in disaster rescues. The existing multi-target detection method based on UWB radar echo signals has low accuracy and has difficulty extracting breathing and heartbeat information at the same time. Therefore, this paper proposes a new multi-target localization and vital sign detection method using ultra-wide band radar. A target recognition and localization method based on permutation entropy (PE) and K means++ clustering is proposed to determine the number and position of targets in the environment. An adaptive denoising method for vital sign extraction based on ensemble empirical mode decomposition (EEMD) and wavelet analysis (WA) is proposed to reconstruct the breathing and heartbeat signals of human targets. A heartbeat frequency extraction method based on particle swarm optimization (PSO) and stochastic resonance (SR) is proposed to detect the heartbeat frequency of human targets. Experimental results show that the PE—K means++ method can successfully recognize and locate multiple human targets in the environment, and its average relative error is 1.83%. Using the EEMD–WA method can effectively filter the clutter signal, and the average relative error of the reconstructed respiratory signal frequency is 4.27%. The average relative error of heartbeat frequency detected by the PSO–SR method was 6.23%. The multi-target localization and vital sign detection method proposed in this paper can effectively recognize all human targets in the multi-target scene and provide their accurate location and vital signs information. This provides a theoretical basis for the technical system of emergency rescue and technical support for post-disaster rescue.

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“…In the use of weighing sensors, the greater interference is the nonlinear, nonsmooth signal generated by the surrounding environment and the circuit itself; the traditional weighing signal processing method can no longer remove the noise in the signal well. In recent years, commonly used processing methods for this type of signals include empirical mode decomposition (EMD) [13,14], wavelet transform (WT) [15][16][17], and deep learning-related methods [18]. Some scholars even change the measurement method; for example, the measurement method of [19] has less noise in the original signal when the wiring faults are located.…”

Section: Introductionmentioning

confidence: 99%

Combined EEMD with a Novel Flexible Wavelet Threshold Function for Weighing Signal Denoising Approach

et al. 2022

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This paper proposes a method of combining ensemble empirical mode decomposition (EEMD) with a novel flexible wavelet threshold function to reduce the random error in the weighing result that caused by the nonstationary and nonlinear noise in quality characteristic parameter measurement equipment. The original signal is first processed by EEMD, and then, all intrinsic mode functions (IMFs) are processed by a novel flexible threshold function proposed by this paper. Finally, the denoised signal is obtained by adding reconstructed IMFs and residual. Through theoretical analysis, the proposed threshold function can retain more useful information and have continuity at the segmentation point. Moreover, the addition of adjustable parameters makes it more adaptable. Its advantages are verified by comparing the denoised results with other threshold functions in the simulation model. In weighing experiment, the validity of the novel flexible threshold function in weighing signal denoising is verified, and the effectiveness of the proposed method is also quantitatively confirmed by comparing the random errors of the original signal and the denoised signal.

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Applications of TVF‐EMD in Vital Signal Detection for UWB Radar

Cited by 9 publications

References 34 publications

Two-Step Accuracy Improvement for Multitarget Detection in Complex Environment Using UWB Radar

Two-Step Accuracy Improvement for Multitarget Detection in Complex Environment Using UWB Radar

A Multi-Target Localization and Vital Sign Detection Method Using Ultra-Wide Band Radar

Combined EEMD with a Novel Flexible Wavelet Threshold Function for Weighing Signal Denoising Approach

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