Title: RAIL: An Accurate and Fast Angle-inferred Localization Algorithm for UAV-WSN Systems Show Chinese title

Title: RAIL：一种针对无人机无线传感器网络系统的精确快速角度推断定位算法

Abstract: Location information is a fundamental requirement for unmanned aerial vehicles (UAVs) and other wireless sensor networks (WSNs). However, accurately and efficiently localizing sensor nodes with diverse functionalities remains a significant challenge, particularly in a hardware-constrained environment. To address this issue and enhance the applicability of artificial intelligence (AI), this paper proposes a localization algorithm that does not require additional hardware. Specifically, the angle between a node and the anchor nodes is estimated based on the received signal strength indication (RSSI). A subsequent localization strategy leverages the inferred angular relationships in conjunction with a bounding box. Experimental evaluations in three scenarios with varying number of nodes demonstrate that the proposed method achieves substantial improvements in localization accuracy, reducing the average error by 72.4% compared to the Min-Max and RSSI-based DV-Hop algorithms, respectively. Show Chinese abstract

Abstract: 位置信息是无人机（UAV）和其他无线传感器网络（WSN）的基本需求。然而，在硬件受限的环境中，准确且高效地定位具有不同功能的传感器节点仍然是一个重大挑战。 为了解决这一问题并增强人工智能（AI）的适用性，本文提出了一种不需要额外硬件的定位算法。具体来说，节点与锚节点之间的角度是基于接收信号强度指示（RSSI）来估计的。随后的定位策略结合了推断出的角度关系和边界框。 在三个具有不同节点数量的场景中的实验评估表明，所提出的方法在定位精度方面取得了显著改进，平均误差分别比Min-Max算法和基于RSSI的DV-Hop算法减少了72.4%。