Author:
Ma Teng,Qi Fugui,Yu Xiao,Jiao Teng,Lv Hao,Wang Jianqi
Abstract
Objective: Non-contact penetrating detection and sensing of human beings (vital signs) through nonmetallic obstacles (ruins, wall, and smog) using the ultra-wideband (UWB) bio-radar plays a significant role in various post-disaster rescue operations in national public security events, like earthquake, building collapse, and factory explosion. In practical application scenarios, the narrowband radio frequency interference (RFI) and surrounding movement interference (SMI) are the two most common and major types of environmental interference (EI), which would cause serious effects on the penetrating detection performance of the UWB bio-radar.Methods: Therefore, through establishing a quantitative and controllable experiment system and combining a proposed quantitative evaluation method, this paper quantitatively investigates and evaluates the influencing characteristics and laws of these two interferences on human vital sign detection using the UWB bio-radar. In the quantitative experiments, two key environmental spatial parameters (interference distance and angle) of interference sources and two kinds of system parameters (main lobe width and time window) of the UWB bio-radar are considered.Results: Numerous experiments at different interference positions and corresponding statistical results demonstrated that both the RFI and SMI have a high negative correlation with the interference distance. Meanwhile, the SMI, which is highly related to the interference angle, could be screened or isolated by the detecting time window and main lobe boundary of UWB bio-radar, but the RFI could not be detected.Discussion: This study shows potential in providing transcendental knowledge and guidance for special EI suppression study, and even for the future design, manufacture, and rational use of the UWB bio-radar, facilitating improvement in the practical performance and effectiveness of the UWB bio-radar search and rescue system.
Subject
General Earth and Planetary Sciences
Cited by
2 articles.
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1. A UWB Radar and Machine Learning-Based Tool for Detecting Victims Through Foliage in Search and Rescue Operations;2024 13th International Conference on Modern Circuits and Systems Technologies (MOCAST);2024-06-26
2. A Modified CA-CFAR Multi-human Detection Algorithm in Complex Environment Using Radar;2024 International Conference on Electronic Engineering and Information Systems (EEISS);2024-01-13