A novel cooperative strategy for distributed unmanned aerial vehicle (UAV) swarms with different functions, namely the mission chain-driven unmanned aerial vehicle swarms cooperation method, is proposed to allow the fast search and timely rescue of injured human targets in a wide-area outdoor environment. First, a UAV-camera unit is exploited to detect the suspected human target combined with improved deep learning technology. Then, the target location information is transferred to a self-organizing network. Then, the special bio-radar-UAV unit was released to recheck the survivals through a respiratory characteristic detection algorithm. Finally, driven by the location and vital sign status of the injured, a nearby emergency-UAV unit will perform corresponding medical emergency missions, such as dropping emergency supplies. Experimental results show that this strategy can identify the human targets autonomously from the outdoor environment effectively, and the target detection, target sensing, and medical emergency mission chain is completed successfully relying on the cooperative working mode, which is meaningful for the future search-rescue mission of outdoor injured human targets.

As an important and basic platform for remote life sensing, unmanned aerial vehicles (UAVs) may hide the vital signals of an injured human due to their own motion. In this work, a novel method to remove the platform motion and accurately extract human respiration is proposed. We utilized a hovering UAV as the platform of ultra-wideband (UWB) radar to capture human respiration. To remove interference from the moving UAV platform, we used the delay calculated by the correlation between each frame of UWB radar data in order to compensate for the range migration. Then, the echo signals from the human target were extracted as the observed multiple range channel signals. Owing to meeting the independent component analysis (ICA), we adopted ICA to estimate the signal of respiration. The results of respiration detection experiments conducted in two different outdoor scenarios show that our proposed method could accurately separate respiration of a ground human target without any additional sensor and prior knowledge; this physiological information will be essential for search and rescue (SAR) missions.