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本文为美国佛罗里达国际大学（作者：Virgilio Acuna）的硕士论文，共54页。

本论文旨在探讨无人机如何利用无线定位成功协助搜救行动。用于捕获/检测WiFi探测器请求的分区网格遵循搜索理论方法中的概念。无人机安装了一个传感器，例如WiFi嗅探器，用于捕捉/检测受害者或失踪人员智能手机的WiFi信号。应用基于随机森林的机器学习算法，用户位置估计的准确率为81.8%。

无人机技术在导航性能和飞行时间上存在局限性，提出了优化这些限制的程序。此外，还分析了无人机在飞行过程中如何操纵，考虑了不同的SAR飞行模式和无人机的锂电池消耗率。结果表明，遥控控制无人机探测到的信号最多，但与自主控制相比，其功耗效率较差。

This thesis presents how unmanned aerial vehicles (UAVs) can successfully assist in search and rescue (SAR) operations using wireless localization. The zone-grid to partition to capture/detect WiFi probe requests follows the concepts found in Search Theory Method. The UAV has attached a sensor, e.g., WiFi sniffer, to capture/detect the WiFi probes from victims or lost people’s smartphones. Applying the Random-Forest based machine learning algorithm, an estimation of the user’s location is determined with a 81.8% accuracy.

UAV technology has shown limitations in the navigational performance and limited flight time. Procedures to optimize these limitations are presented. Additionally, how the UAV is maneuvered during flight is analyzed, considering different SAR flight patterns and Li-Po battery consumption rates of the UAV. Results show that controlling the UAV by remote-controll detected the most probes, but it is less power efficient compared to control it autonomously.

1.   引言
   

2. 文献回顾
3. 利用无人机探测请求定位WIFI设备
4. 无人机使用WIFI探测请求延长搜索和救援行动的飞行时间
5. 结论

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