Effective use of GPS and mobile networks for localisation in rangeland areas is constrained by their high power consumption and high deployment costs. Long-range (LoRa), a low-power wide area network (LPWAN) technology, can be employed to mitigate these challenges. In contrast to prior research where the prevalent approaches entail multiple gateways. This work proposes a valuable methodology focused on a single mobile LoRa gateway for localisation. A particle filtering and machine learning-based pipeline is employed to map the distance between a target node and the gateway from the received signal strength indicator (RSSI). Particle filtering is used to reduce the impact of noise on the RSSI values. Then, several machine learning techniques, such as support vector machines, random forest, and k-nearest neighbour, are used on the RSSI values to estimate the distance. The estimated distance is then used for tracking using a centroid pseudo-trilateration method. The proposed method was tested in a real-world semi-line-of-sight setting, using three datasets generated by LoRaWAN-specified hardware components and a server. Two forms of experiments were performed: active searching and passive monitoring. We propose an iterative estimation process to address the dilution of precision caused by the initial positions of the gateway required for active searching applications. The results show that active searching typically requires 2 to 3 hops to reach a target node. The accuracy of passive monitoring depends on the proximity of the gateway, which varies from 20 m to 170 m. This proposed approach has the potential to open the way for localising, tracking, or monitoring target objects within sparsely populated rangeland areas, even when resources are severely constrained.

中文翻译：

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使用单一移动网关的 LoRa 本地化

GPS 和移动网络在牧场区域的有效定位受到高功耗和高部署成本的限制。可以采用低功耗广域网 (LPWAN) 技术远程 (LoRa) 来缓解这些挑战。与之前的研究相比，流行的方法需要多个网关。这项工作提出了一种有价值的方法，重点关注用于本地化的单个移动 LoRa 网关。采用粒子过滤和基于机器学习的管道根据接收信号强度指示器 (RSSI) 映射目标节点和网关之间的距离。粒子滤波用于减少噪声对 RSSI 值的影响。然后，对 RSSI 值使用多种机器学习技术（例如支持向量机、随机森林和 k 最近邻）来估计距离。然后使用估计的距离使用质心伪三边测量方法进行跟踪。所提出的方法在现实世界的半视距设置中进行了测试，使用由 LoRaWAN 指定的硬件组件和服务器生成的三个数据集。进行了两种形式的实验：主动搜索和被动监测。我们提出了一种迭代估计过程来解决主动搜索应用程序所需的网关初始位置引起的精度降低问题。结果表明，主动搜索通常需要 2 到 3 跳才能到达目标节点。被动监控的精度取决于网关的远近程度，范围从 20 m 到 170 m。这种提出的方​​法有可能为在人口稀少的牧场区域内定位、跟踪或监测目标物体开辟道路，即使在资源严重受限的情况下也是如此。