Disaster management, search and rescue missions, and health monitoring are examples of critical applications that require object localization with high precision and sometimes in a timely manner. In the absence of the global positioning system (GPS), the radio received signal strength index (RSSI) can be used for localization purposes due to its simplicity and cost-effectiveness. However, due to the low accuracy of RSSI, unmanned aerial vehicles (UAVs) or drones may be used as an efficient solution for improved localization accuracy due to their agility and higher probability of line-of-sight (LoS). Hence, in this context, we propose a novel framework based on reinforcement learning (RL) to enable a UAV (agent) to autonomously find its trajectory that results in improving the localization accuracy of multiple objects in shortest time and path length, fewer signal-strength measurements (waypoints), and/or lower UAV energy consumption. In particular, we first control the agent through initial scan trajectory on the whole region to 1) know the number of nodes and estimate their initial locations, and 2) train the agent online during operation. Then, the agent forms its trajectory by using RL to choose the next waypoints in order to minimize the average location errors of all objects. Our framework includes detailed UAV to ground channel characteristics with an empirical path loss and log-normal shadowing model, and also with an elaborate energy consumption model. We investigate and compare the localization precision of our approach with existing methods from the literature by varying the UAV's trajectory length, energy, number of waypoints, and time. Furthermore, we study the impact of the UAV's velocity, altitude, hovering time, communication range, number of maximum RSSI measurements, and number of objects. The results show the superiority of our method over the state-of-art and demonstrates its fast reduction of the localization error.

中文翻译：

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用于定位地面目标的自主无人机轨迹：一种强化学习方法

灾难管理、搜救任务和健康监测是关键应用的示例，这些应用需要高精度且有时及时地进行对象定位。在没有全球定位系统 (GPS) 的情况下，无线电接收信号强度指数 (RSSI) 由于其简单性和成本效益可用于定位目的。然而，由于 RSSI 的准确性较低，无人驾驶飞行器 (UAV) 或无人机由于其敏捷性和更高的视线 (LoS) 概率，可用作提高定位精度的有效解决方案。因此，在这种情况下，我们提出了一种基于强化学习 (RL) 的新框架，使 UAV（代理）能够自主找到其轨迹，从而在最短的时间和路径长度、更少的信号强度测量（航点）内提高多个物体的定位精度，和/或降低无人机能耗。特别是，我们首先通过对整个区域的初始扫描轨迹来控制代理，以 1) 知道节点的数量并估计它们的初始位置，以及 2) 在操作过程中在线训练代理。然后，代理通过使用 RL 选择下一个路点来形成其轨迹，以最小化所有对象的平均位置误差。我们的框架包括详细的无人机到地面通道特性，具有经验路径损耗和对数正态阴影模型，以及精心设计的能耗模型。我们通过改变无人机的轨迹长度、能量、航路点数量和时间来研究和比较我们的方法与文献中现有方法的定位精度。此外，我们研究了无人机的速度、高度、悬停时间、通信范围、最大 RSSI 测量数量和物体数量的影响。结果表明我们的方法优于现有技术，并证明了其快速减少定位误差。