Victim Detection and Localization for Search-and-Rescue: A Robot-Mounted UWB Radar with a Hybrid CNN–ViT Model

Robotic systems for search-and-rescue operations require robust, non-line-of-sight victim detection in order to locate trapped individuals behind obstacles with high precision. This paper presents a robotic victim-localization system based on a convolutional neural network—vision transformer (CNN-ViT) architecture trained on an open-source radar dataset for through-wall presence detection. In addition to binary presence detection, the proposed approach uses attention information from the transformer layers to estimate the region of the radar signal most relevant to the victim location. The model is deployed on a robotic platform and tested in an additional realistic environment, where classification and distance-estimation outputs are fused into a heatmap-style spatial representation. This enables the system to localize the estimated victim position on the map generated by the robot. To enhance robustness, the system is evaluated using both a leave-one-subject-out (LOSO) protocol on the original open-source radar dataset and additional experimental sessions collected with the robotic platform. On the original dataset, the model achieved victim-detection F1 scores of 82–96% and distance-estimation MAE of 0.25–0.65 m relative to the robot. On newly collected, previously unseen data, it achieved F1 scores of 72–92% and an MAE of 0.16–0.78 m on correctly classified present samples. The complete pipeline was further deployed on a mobile robot in an environment different from the one used to collect the original dataset, illustrating the potential of the proposed system for practical search-and-rescue scenarios.