Trapped human detection in search and rescue (SAR) scenarios poses a
significant challenge in pervasive computing. This study addresses this issue
by leveraging machine learning techniques, given their high accuracy. However,
accurate identification of trapped individuals is hindered by the curse of
dimensionality and noisy data. Particularly in non-line-of-sight (NLOS)
situations during catastrophic events, the curse of dimensionality may lead to
blind spots due to noise and uncorrelated values in detections. This research
focuses on harmonizing information through wireless communication and
identifying individuals in NLOS scenarios using ultra-wideband (UWB) radar
signals. Employing independent component analysis (ICA) for feature extraction,
the study evaluates classification performance using ensemble algorithms on
both static and dynamic datasets. The experimental results demonstrate
categorization accuracies of 88.37% for static data and 87.20% for dynamic
data, highlighting the effectiveness of the proposed approach. Finally, this
work can help scientists and engineers make instant decisions during SAR
operations.

在搜索与救援（SAR）场景中，困人的检测是普适计算中面临的重大挑战，本研究采用机器学习技术来解决这个问题，通过利用无线通信协调信息并使用超宽带雷达信号在非直线可视（NLOS）场景中识别个体，实验结果表明所提出的方法在静态数据和动态数据上分别获得了 88.37% 和 87.20% 的分类准确性，对 SAR 行动中科学家和工程师做出即时决策具有积极意义。