Feasibility of Impulse Radar-Based Through-Wall Imaging for Human Detection in Search and Rescue: A Study on Accuracy, Material Penetration, and Deep Learning Integration

• Published in: International Conference on Electronics, Computers and Artificial Intelligence (Online) pp. 1 - 5
• Main Authors: Ylaya, Vrian Jay, Sinugbuhan, Sharralouze, Becena, Jovin Jecharr
• Format: Conference Proceeding
• Language: English
• Published: IEEE 26.06.2025
• Subjects: Accuracy; Building materials; Building Materials Penetration; Convolutional neural networks; Deep learning; Deep Learning Algorithms; Detection Accuracy Optimization; Emergency services; Imaging; Impulse Radar Technology; Radar; Radar antennas; Radar detection; Radar imaging; Through-Wall Imaging; Urban Search and Rescue (SAR)
• ISSN: 2688-0253
• DOI: 10.1109/ECAI65401.2025.11095454

This study investigates the feasibility of a portable impulse radar-based through-wall imaging system integrated with deep learning algorithms to enhance urban search and rescue (SAR) operations. The system addresses critical limitations in traditional detection methods during disaster scenarios by employing ultra-wideband impulse radar technology coupled with specialized antennas and PicoR 5.0 software. This combination enables the penetration of diverse building materials, including concrete (10 cm), wood, and fiber cement. Through controlled experimental trials and field tests, researchers evaluated detection accuracy and response time across varying wall thicknesses and material compositions, utilizing Convolutional Neural Networks (CNN) for advanced signal processing and target identification. The system demonstrated an overall detection accuracy of 89%, with material-specific performance variations: concrete walls showed reduced precision (70%) but maintained high recall (93%). In comparison, wood and open spaces achieved exceptional precision, exceeding 97%. Deep learning integration proved crucial, improving system robustness against environmental interference and enabling the identification of stationary subjects through micro-movements associated with breathing. Field validation using a portable cart-mounted unit confirmed operational viability across multiple scenarios, successfully detecting moving adults/children and static individuals through 10 cm concrete barriers. Key findings from 320 test cases revealed an average F 1 score of 89% across materials, with confusion matrix analysis showing 285 correct classifications. The technology's effectiveness correlated strongly with material permittivity and signal-to-noise ratio optimization. These results position impulse radar combined with machine learning as a transformative tool for emergency response, providing real-time situational awareness in collapsed structures.