Accurate sensing of multiple humans buried under rubble using IR-UWB SISO radar during search and rescue

Sarkar, Amit; Ghosh, Debalina

doi:10.1016/j.sna.2022.113975

Cited by 15 publications

(3 citation statements)

References 19 publications

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“…the frequency domain, arctangent demodulation, ensemble empirical mode decomposition-based frequency, accumulation method, discrete wavelet transform, convolutional neural networks and variable mode decomposition, etc. and various tasks are addressed, mainly i) the estimation of the breathing and heart rate, obtaining a less than 10% error compared with ground truth measurements [1- 10,[12][13][14]17] ii) the distance between the victim and the radar, obtaining an error of up to several cm [2,[4][5][6][7][8]10,[13][14][15][16] iii) the number of humans within the scanned area, attaining high accuracy in the particular cases [11], [14] and iv) the designation of human presence or absence [15,16].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Human Life Detection behind Walls Exploiting a UWB Radar Sensor

Uzunidis,

Kasnesis,

Patrikakis

et al. 2024

Preprint

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The existence of low-cost and accurate tools that can equip the First Responders (FRs) to detect trapped victims and provide insights about their health condition in the aftermath of a disaster, is imperative. To address the problem of detecting trapped victims behind walls or debris, we have developed a tool that exploits an Ultra-Wide Band (UWB) radar sensor for data collection and Machine Learning (ML) algorithms for data analysis. To evaluate the efficacy of our approach, we collected data from nine humans, both in standing and lying down positions. Next, we applied various ML algorithms to the collected dataset for two discrete sub-tasks that are of interest from an FR’s perspective. The first task is the detection of the victim’s presence, where the algorithms attained more than 95% accuracy and F1-Score. The second task is the estimation of the distance between the radar sensor and the victim, where the tool showed an average error of less than 40 cm.

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Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Human Life Detection behind Walls Exploiting a UWB Radar Sensor

Uzunidis,

Kasnesis,

Patrikakis

et al. 2024

Preprint

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“…In terms of research on multi-target detection methods, Zhang Yang proposed a variety of methods such as time-frequency analysis and improved constant false alarm rate (CFAR) to recognize multiple human targets at different distances [20,21]. Some researchers used the VMD algorithm to detect multiple human targets located at the same distance in an indoor environment and provide breathing frequency information [22,23]. Liu proposed a signal processing algorithm based on source separation and empirical mode decomposition to recognize and locate two subjects buried at different depths, which was able to extract the breathing features of the subjects but failed to capture the heartbeat frequency information [24].…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Cheng

et al. 2023

Sensors

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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 2020, Shunqiao Sun and Athina P. Petropulu used matrix completion to compute the corresponding virtual uniform linear array (ULA) before estimating the target angle [4][5][6]. However, they verified the effectiveness of the ULA using only FFT and MUSIC.…”

Section: Introductionmentioning

confidence: 99%

Research on a Super-Resolution and Low-Complexity Positioning Algorithm Using FMCW Radar Based on OMP and FFT in 2D Driving Scene

Guo

Shen

Deng

et al. 2023

Sensors

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Multitarget positioning technology, such as FMCW millimeter-wave radar, has broad application prospects in autonomous driving and related mobile scenarios. However, it is difficult for existing correlation algorithms to balance high resolution and low complexity, and it is also difficult to ensure the robustness of the positioning algorithm using an aging antenna. This paper proposes a super-resolution and low-complexity positioning algorithm based on the orthogonal matching pursuit algorithm that can achieve more accurate distance and angle estimation for multiple objects in a low-SNR environment. The algorithm proposed in this paper improves the resolving power by two and one orders of magnitude, respectively, compared to the classical FFT and MUSIC algorithms in the same signal-to-noise environment, and the complexity of the algorithm can be reduced by about 25–30%, with the same resolving power as the OMP algorithm. Based on the positioning algorithm proposed in our paper, we use the PSO algorithm to optimize the arrangement of an aging antenna array so that its angle estimation accuracy is equivalent to that observed when the antenna is intact, improving the positioning algorithm’s robustness. This paper also further realizes the use of the proposed algorithm and a single-frame intermediate frequency signal to estimate the position angle information of the object and obtain its motion trajectory and velocity, verifying the proposed algorithm’s estimation ability when it comes to these qualities in a moving scene. Furthermore, this paper designs and carries out simulations and experiments. The experimental results verify that the positioning algorithm proposed in this paper can achieve accuracy, robustness, and real-time performance in autonomous driving scenarios.

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Accurate sensing of multiple humans buried under rubble using IR-UWB SISO radar during search and rescue

Cited by 15 publications

References 19 publications

Machine Learning-Based Human Life Detection behind Walls Exploiting a UWB Radar Sensor

Machine Learning-Based Human Life Detection behind Walls Exploiting a UWB Radar Sensor

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

Research on a Super-Resolution and Low-Complexity Positioning Algorithm Using FMCW Radar Based on OMP and FFT in 2D Driving Scene

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