Collaborative Multi-UAV Data Fusion for SAR Applications with Moving Targets

Unmanned Aerial Vehicles (UAVs) are improving considerably search and rescue (SAR) operations by providing unprecedented capabilities in dynamic and hazardous environments. This study presents an innovative, collaborative multi-UAV data fusion approach that addresses the critical challenge of locating multiple moving targets within strict time constraints. This approach improves traditional search techniques by incorporating intelligent information sharing, fusion, and coordinated path planning. The core innovation of the algorithm lies in its ability to dynamically and collaboratively predict the geographical zones with the highest probability of needed rescue operations. This enables the group of UAVs to coordinate and optimize their search strategies in real-time. This research offers valuable insights into multi-UAV collaboration through high-fidelity simulations involving more than 600 different scenarios with UAV swarms and moving ground targets. The experimental results indicate that their effectiveness significantly improves as the number of UAVs increases, following a quadratic trend until it reaches a plateau. In particular, the accuracy rate remains above 90%, regardless of the number of UAVs after reaching the plateau. This suggests that while a higher density of UAVs enhances search efficiency, larger UAV swarms yield diminishing returns. Notably, the approach shows superior efficiency in environments with clustered targets, which makes it particularly suitable for disaster response scenarios that involve more concentrated target locations.