  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.
