An optimized UAV trajectory planning for localization in disaster scenarios

Unmanned aerial vehicles (UAVs) are considered one of the most promising emerging technologies to support rescue teams in disaster management and relief operations according to UN and Red Cross reports. In this work, we consider a disaster scene with damaged communication infrastructure and leverage UAVs for efficient and accurate positioning of potential survivors through the seamless collection of the received signal strength indicators (RSSI) of their mobile devices. We assume the scene is divided into multiple regions or cells with varying levels of importance based on the damage degree or the population density for example, and, thus, requiring different localization effort to improve the achieved accuracy. We formulate and solve two complementary subproblems. The first subproblem identifies a minimal number of strategic positions, referred to as waypoints or scanning points, at which the UAV hovers to collect the required number of RSSI signals from all devices within each cell in the disaster scene. Cells assigned higher importance levels call for higher number of RSSI readings from their devices. The waypoints generated from the first subproblem are then input to the second subproblem that constructs an efficient UAV trajectory that traverses all waypoints. By the end of the UAV mission, the collected RSSI measurements are processed to localize the discovered devices while taking into account the wireless channel statistical variability. Simulation results are generated and analyzed to demonstrate the accuracy and effectiveness of the proposed solution approach in localizing an unknown number of mobile devices in disaster scenes with regions of varying importance levels. In addition, an experimental testbed is designed and implemented as a proof of concept to validate the practicality of implementing the proposed localization solution in a realistic setting.