基于光学的目标定位技术直接依赖于光线和视角，其他基于声光电的感知技术也都存在不同局限性，因此在有浓烟高温的火灾等复杂场景中难以应用。近年来，基于无线信号的目标感知技术由于其环境普适性，已突显应用潜力。然而，现有无线定位方法受制于其通信半径和穿透能力，也难以直接用于解决火灾场景的目标定位。低功耗远距离无线通信技术LoRa的诞生和无人机技术的成熟为上述问题提供了一条可行途径。本课题旨在尝试寻求建立天线波束宽度与感知分辨率、系统鲁棒性与感知延时之间的折衷优化；突破更为复杂的多径效应，最终提出大规模复杂场景中低成本LoRa设备与无人机相结合的远距离、大范围目标感知定位方法。项目的本质是探索大规模复杂环境下信号时空特征与移动收发端设备和移动目标之间的内在联系，寻求大规模复杂环境下的目标定位有效途径。成果将为火灾救援等灾害场景提供有价值的参考方案，对推动物联网应用有重要意义。

The optical-based target location technology replies on the light and perspective. The audio-optic sensing technologies have different limitations for target localization. Thus, it is difficult to apply existing solutions in complex scenes such as fires with dense smoke and high temperature. In recent years, the target sensing technology based on wireless signal becomes more and more attractive due to its adaptive in environment. However, the existing wireless location method is limited by its communication radius and penetration ability, and it is difficult to be directly used to solve the target location in fire scenarios. The low-power long-distance wireless communication technology LoRa and the uav technology provide a feasible way to solve the above problems. This project attempts to establish the compromise optimization between the antenna beam width and the perceptual resolution, and also the optimization between system robustness and perceptual delay. It breaks through complex multipath effects. It finally proposes a long-distance and wide-range target location-awarded method by combining the low-cost LoRa and the uav in large-scale complex scenes. The essence of this project is to (1) explore the internal relationship among the space-time characteristics of signals, the mobile devices and the mobile targets in large-scale complex scenes, and (2) seek an effective way to locate targets in large-scale complex scenes. The results will provide some valuable references for the fire rescue and other disaster scenarios. It also can improve the development of the application of the Internet of things.