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Emergency Response Person Localization and Vital Sign Estimation Using a Semi-Autonomous Robot Mounted SFCW Radar.

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    Area of Science:

    • Robotics and Autonomous Systems
    • Radar Signal Processing
    • Disaster Response Technologies

    Background:

    • Increasing frequency and scale of disasters necessitate advanced search and rescue (SAR) technologies.
    • Semi-autonomous robots offer solutions for accessing dangerous and inaccessible environments.
    • Radar-based approaches are crucial for SAR in degraded visual or non-line-of-sight conditions.

    Purpose of the Study:

    • To develop and validate a comprehensive signal processing chain for radar-based multi-person detection.
    • To enable accurate 2D localization and breathing frequency estimation of individuals in complex environments.
    • To assess the performance of the system on a challenging, real-world emergency response dataset.

    Main Methods:

    • Implementation of a complete signal processing chain for radar data.
    • Utilized a semi-autonomous robot equipped with a commercial through-wall radar system.
    • Collected a dataset of 62 diverse scenarios with up to five persons, including obstacles.

    Main Results:

    • Demonstrated successful multi-person detection and 2D-MUSIC localization.
    • Achieved reliable breathing frequency estimation from radar signals.
    • Showcased promising performance on a dataset featuring various postures, ranges, and line-of-sight obstructions.

    Conclusions:

    • The proposed radar-based signal processing chain is effective for search and rescue operations.
    • The system provides valuable, otherwise unobtainable, information in challenging disaster scenarios.
    • The developed dataset serves as a valuable resource for future research in robotic SAR.