AN An ica-ensemble learning approach for prediction of uwb nlos signals data classification
This work addresses the challenge of accurate human detection in noisy, high-dimensional NLOS environments for search and rescue operations, representing an incremental improvement.
The study tackled the problem of detecting trapped humans in search and rescue scenarios using UWB radar signals in non-line-of-sight conditions, achieving classification accuracies of 88.37% for static data and 87.20% for dynamic data.
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.