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<p>This paper presents a hybrid victim search system that integrates ultra-wideband (UWB) and radio frequency (RF) technologies to overcome the limitations of conventional indoor search-and-rescue operations under degraded visibility conditions, such as smoke-filled environments. The proposed system employs RF communication for long-range scanning to identify active victim tags and their unique IDs, while UWB is selectively activated for short-range, high-precision localization when the victim’s inertial measurement unit (IMU) data indicates significant motion. An adaptive Kalman filter (AKF) is applied to RF-based range measurements to improve accuracy by dynamically updating noise variance in real time. The system was implemented and validated in simulation environment representing a firefighter and multiple victims. Performance was quantitatively evaluated using root mean square error (RMSE) of distance estimation, victim detection time, total search duration, and travel distance. Experimental results show that the AKF-enhanced RF ranging significantly improves distance estimation accuracy, reducing RMSE by up to 43.5% compared with conventional RF measurements, and shortens search time by up to 21.4%. These findings demonstrate that the proposed hybrid UWB-RF system can substantially enhance situational awareness and operational efficiency in complex indoor disaster scenarios, providing a practical and scalable approach for real-world deployment.</p>

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Hybrid UWB-RF Victim Search With Adaptive Kalman Filtering Under Dense Smoke Conditions

  • 期刊论文

  • 发表日期：

    2025年11月4日

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  • Sun-Ho Jang,
  • Yong-Jun Cho,
  • Bo-Kyu Kwon,
  • Myo-Taeg Lim

摘要

This paper presents a hybrid victim search system that integrates ultra-wideband (UWB) and radio frequency (RF) technologies to overcome the limitations of conventional indoor search-and-rescue operations under degraded visibility conditions, such as smoke-filled environments. The proposed system employs RF communication for long-range scanning to identify active victim tags and their unique IDs, while UWB is selectively activated for short-range, high-precision localization when the victim’s inertial measurement unit (IMU) data indicates significant motion. An adaptive Kalman filter (AKF) is applied to RF-based range measurements to improve accuracy by dynamically updating noise variance in real time. The system was implemented and validated in simulation environment representing a firefighter and multiple victims. Performance was quantitatively evaluated using root mean square error (RMSE) of distance estimation, victim detection time, total search duration, and travel distance. Experimental results show that the AKF-enhanced RF ranging significantly improves distance estimation accuracy, reducing RMSE by up to 43.5% compared with conventional RF measurements, and shortens search time by up to 21.4%. These findings demonstrate that the proposed hybrid UWB-RF system can substantially enhance situational awareness and operational efficiency in complex indoor disaster scenarios, providing a practical and scalable approach for real-world deployment.