AN An ica-ensemble learning approach for prediction of uwb nlos signals data classification

TL;DR

This paper presents an ICA-ensemble learning method for classifying UWB NLOS signals to detect trapped humans in SAR scenarios, improving accuracy despite noisy and high-dimensional data.

Contribution

It introduces a novel combination of independent component analysis with ensemble classifiers for UWB NLOS signal classification in rescue operations.

Findings

Achieved 88.37% accuracy on static data

Achieved 87.20% accuracy on dynamic data

Demonstrated effectiveness in noisy, high-dimensional environments

Abstract

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.