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.

The absence of a key stratum during overburden rock movement is crucial to the mining pressure of fully mechanized coal mining faces. Using physical and numerical simulations, the 21304 mechanized mining in Daliuta and Huojitu coal mining faces 1−2 appeared twice during a pressure frame accident analysis. The results indicate that a lack of key overlying strata is crucial to the mining of lower coal seams, particularly for the upper sections of a single key stratum of coal. When the key stratum of the upper coal seam is absent, a stable masonry structure is formed after mining. It is easy to form stable stacked strata at the bottom of a coal seam. When developing gullies in deep terrains, the formation of the key stratum will be an upper rock fracture affected by the impact, resulting in a partial absence of the key stratum. When the key stratum is absent, the mining of upslope working faces and the probability of dynamic strata pressure increase with the overburden on the working face and mining of downslope faces. The face mine pressure development laws on the upper and lower coal seam mining were similar, mainly manifesting as “slope section >valley bottom section >back slope section.”