Obstacle-Aware Human Localization via Channel Impulse Response

Search-and-rescue (SAR) operations in collapsed buildings requires sensing systems that are reliable, fast, and robust to cluttered environments. This paper introduces OHL-CIR, a simulation-driven framework for RF-based human detection and localization beneath rubble. By generating a large and diverse dataset of channel impulse responses (CIRs), the framework enables reproducible evaluation across multiple rubble scenarios. OHL-CIR employs a two-stage inference pipeline: binary classification for victim presence, followed by regression for 2D localization. Results show detection accuracy above 95% across all categories and sub-centimeter localization errors. A precision–stability tradeoff is observed: Gradient Boosting achieves the finest precision in structured settings, while Random Forest maintains robustness under diverse rubble conditions. CIR analyses confirm physics-consistent perturbations and density maps demonstrate how predictions can guide UAV search paths. These findings position OHL-CIR as both accurate and interpretable, with strong potential for deployment in UAV-assisted SAR and broader applications in IoT localization and future 6G systems.