As a new method to detect vital signs, Ultra-wideband (UWB) radar could continuously monitor human respiratory signs without contact. Aimed at addressing the problem of large interference and weak acquisition signal in radar echo signals from complex scenes, this paper adopts a UWB radar echo signal processing method that combines strong physical sign information extraction at P time and Variational Mode Decomposition (VMD) to carry out theoretical derivation. Using this novel processing scheme, respiration and heartbeat signals can be quickly reconstructed according to the selection of the appropriate intrinsic mode functions (IMFs), and the real-time detection accuracy of human respiratory signs is greatly improved. Based on an experimental platform, the data collected by the UWB radar module were first verified against the measured values obtained at the actual scene. The results of a validation test proved that our UWB radar echo signal processing method effectively eliminated the respiratory clutter signal and realized the accurate measurement of respiratory and heartbeat signals, which would prove the existence of life and further improve the quality of respiration and heartbeat signal and the robustness of detection.

The identification of weak vital signs has always been one of the difficulties in the field of life detection. In this paper, a novel vital sign detection and extraction method with high efficiency, high precision, high sensitivity and high signal-to-noise ratio is proposed. Based on the NVA6100 pulse radar system, the radar matrix which contains several radar pulse detection signals is received. According to the characteristics of vital signs and radar matrices, the Singular Value Decomposition (SVD) is adopted to perform signal denoising and decomposition after preprocessing, and the temporal and spatial eigenvectors of each principal component are obtained. Through the energy proportion screening, the Wavelet Transform decomposition and linear trend suppression, relatively pure vital signs in each principal component, are obtained. The human location is detected by the Energy Entropy of spatial eigenvectors, and the respiratory signal and heartbeat signal are restored through a Butterworth Filter and an MTI harmonic canceller. Finally, through an analysis of the performance of the algorithm, it is proved to have the properties of efficiency and accuracy.

Due to high gas content and a low permeability coefficient in deep coal seam mining, the spontaneous combustion of coal around the wellbore can easily occur, leading to difficulties in extracting gas during the mining process. To determine the dangerous area around the borehole and conduct advanced prevention and control measures are the keys to preventing spontaneous combustion in boreholes. However, the dangerous area around the borehole is not clear, the sealing parameters lack scientific basis, and the key prevention and control measures are not clear, which have caused great harm to coal mines. This study took the 24,130 working face of Pingdingshan No. 10 Mine as an example, using numerical simulation, theoretical analysis, and field tests to classify the risks of studying the surrounding area of the wellbore. The dangerous area variations under different lengths of shotcrete in the roadway were analyzed, the optimal plugging parameters were studied, and the current “two plugs and one injection” plugging device was optimized. Based on the oxygen concentration and air leakage rate, a method was proposed to divide the dangerous area of fissure coal spontaneous combustion around the borehole induced by gas extraction. The dangerous area of spontaneous combustion around the borehole was defined as having an oxygen concentration larger than 7% and an air leakage rate less than 0.004 m/s. The comprehensive control measures of the grouting length at 2–4 m, hole-sealing parameter at 20-13 (hole-sealing depth 20 m, hole-sealing length 13 m) and the “two plugs, one injection and one row” device were determined.

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