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.

根据联合国和红十字会的报告，无人飞行器（UAV）被认为是在灾害管理和救灾行动中支持救援队的最有前途的新兴技术之一。在这项工作中，我们考虑了通信基础设施受损的灾难现场，并通过无缝收集移动设备的接收信号强度指标（RSSI），利用无人机来有效，准确地定位潜在的幸存者。我们假设场景基于损伤程度或人口密度被划分为具有不同重要程度的多个区域或单元，因此需要不同的定位努力来提高所达到的精度。我们制定并解决两个互补的子问题。第一个子问题确定了最少数量的战略职位，称为航路点或扫描点，UAV会在该点徘徊以从灾难现场每个小区内的所有设备收集所需数量的RSSI信号。分配较高重要性级别的单元需要从其设备中读取更多RSSI。然后，将从第一个子问题生成的航点输入到第二个子问题，该第二个子问题构建遍历所有航点的有效UAV轨迹。到无人机任务结束时，在考虑无线信道统计差异的同时，处理收集到的RSSI测量值以定位发现的设备。生成并分析了仿真结果，以证明所提出的解决方案方法在灾难场景中重要性级别不同的区域中定位未知数量的移动设备时的准确性和有效性。