The large number and scale of natural and man-made disasters have led to an urgent demand for technologies that enhance the safety and efficiency of search and rescue teams. Semi-autonomous rescue robots are beneficial, especially when searching inaccessible terrains, or dangerous environments, such as collapsed infrastructures. For search and rescue missions in degraded visual conditions or non-line of sight scenarios, radar-based approaches may contribute to acquire valuable, and otherwise unavailable information. This article presents a complete signal processing chain for radar-based multi-person detection, 2D-MUSIC localization and breathing frequency estimation. The proposed method shows promising results on a challenging emergency response dataset that we collected using a semi-autonomous robot equipped with a commercially available through-wall radar system. The dataset is composed of 62 scenarios of various difficulty levels with up to five persons captured in different postures, angles and ranges including wooden and stone obstacles that block the radar line of sight. Ground truth data for reference locations, respiration, electrocardiogram, and acceleration signals are included. The full emergency response benchmark data set as well as all codes to reproduce our results, are publicly available at https://doi.org/10.21227/4bzd-jm32.

翻译：自然灾害与人为灾害的数量和规模不断增加，对提升搜救团队安全性和效率的技术产生了迫切需求。半自主救援机器人特别适用于搜索难以进入的地形或危险环境（如倒塌基础设施）。在视觉条件退化或非视距场景的搜救任务中，基于雷达的方法有助于获取宝贵且原本无法获得的信息。本文提出了一套完整的基于雷达的多人员检测、2D-MUSIC定位及呼吸频率估计信号处理链。所提方法在我们使用配备商用穿墙雷达系统的半自主机器人采集的具有挑战性的应急响应数据集上展现出良好效果。该数据集包含62个不同难度级别的场景，最多可捕获五名人员在不同姿势、角度和距离下的信息，并包含阻挡雷达视线的木质和石质障碍物。数据集中包含参考位置、呼吸、心电图和加速度信号的基准真值数据。完整的应急响应基准数据集及用于复现我们结果的所有代码均已公开，可通过https://doi.org/10.21227/4bzd-jm32获取。