Abstract
Distinguishing humans from animals using ultra-wideband (UWB) radar is necessary in post-disaster emergency rescues to prioritize and thereby optimize the distribution of labor and material resources. However, current studies are few and have only been implemented in simple laboratory environments, such that the effectiveness of these approaches cannot be guaranteed in rescue situations. This study describes experiments under actual post-disaster emergency rescue scenarios, for which the signal-to-noise ratio of UWB radar is seriously degraded owing to multipath effects and a complicated ruin environment. Four distinguishing features are extracted from aspects of wavelet entropy, correlation coefficient, and energy to classify humans from animals. Analysis of feature effectiveness showed that each feature could identify humans from animals individually. The largest difference between humans and animals was found in a feature which combines advantages of the correlation coefficient and energy simultaneously. There was no overlap between the human and animal values for this feature among the 20 sets of radar data collected. This is the first attempt to distinguish humans from animals in an actual post-disaster trapped condition, and it yielded four features of strong classification ability. We envision this study to advance real-world applicability of UWB radar in post-disaster emergency rescue.
Highlights
Ultra-wideband (UWB) radar has gained increasing research interest for its potential in many civilian and military applications, mainly because of its advantages penetrating obstacles [1,2,3,4]
We found that the five largest correlation coefficients between Sop and signal in the target position (Stp) were large enough to be statistically relevant for humans, where the smallest value among them was usually larger than 0.6
The human target is surrounded by ruins in the illumination direction of the UWB radar where the multipath effect is significant
Summary
Ultra-wideband (UWB) radar has gained increasing research interest for its potential in many civilian and military applications, mainly because of its advantages penetrating obstacles [1,2,3,4]. UWB radar can provide high-precision localization in through-obstacle situations and possesses excellent time resolution. Applications such as gesture recognition [5], medical monitoring [6], target imaging [7], and target tracking [8] using UWB radar have been possible and show considerable market potential. A combined Gaussian mixture and hidden Markov model has been used to identify slow-moving humans from animals to detect potential poachers in nature reserves [11]. These types of studies are not suitable for post-disaster emergency rescue applications because survivors are often trapped under obstacles of building ruins where they cannot move freely. Through-obstacle penetration and target stationary status are fundamental features in post-disaster rescue scenarios
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