Ponte Salvatore, Ariante Gennaro, Greco Alberto, Del Core Giuseppe
Department of Engineering, University of Campania "L. Vanvitelli", 81031 Aversa, Italy.
Department of Science and Technology, University of Naples "Parthenope", 80133 Naples, Italy.
Sensors (Basel). 2024 Nov 8;24(22):7170. doi: 10.3390/s24227170.
Localization of unmanned aircraft systems (UASs) in indoor scenarios and GNSS-denied environments is a difficult problem, particularly in dynamic scenarios where traditional on-board equipment (such as LiDAR, radar, sonar, camera) may fail. In the framework of autonomous UAS missions, precise feedback on real-time aircraft position is very important, and several technologies alternative to GNSS-based approaches for UAS positioning in indoor navigation have been recently explored. In this paper, we propose a low-cost IPS for UAVs, based on Bluetooth low energy (BLE) beacons, which exploits the (received signal strength indicator) for distance estimation and positioning. Distance information from measured values can be degraded by multipath, reflection, and fading that cause unpredictable variability of the and may lead to poor-quality measurements. To enhance the accuracy of the position estimation, this work applies a differential distance correction (DDC) technique, similar to differential GNSS (DGNSS) and real-time kinematic (RTK) positioning. The method uses differential information from a reference station positioned at known coordinates to correct the position of the rover station. A mathematical model was established to analyze the relation between the and the distance from Bluetooth devices (Eddystone BLE beacons) placed in the indoor operation field. The master reference station was a Raspberry Pi 4 model B, and the rover (unknown target) was an Arduino Nano 33 BLE microcontroller, which was mounted on-board a UAV. Position estimation was achieved by trilateration, and the extended Kalman filter (EKF) was applied, considering the nonlinear propriety of beacon signals to correct data from noise, drift, and bias errors. Experimental results and system performance analysis show the feasibility of this methodology, as well as the reduction of position uncertainty obtained by the DCC technique.
在室内场景和全球导航卫星系统（GNSS）信号被遮挡的环境中，无人机系统（UAS）的定位是一个难题，尤其是在动态场景中，传统的机载设备（如激光雷达、雷达、声纳、摄像头）可能会失效。在自主无人机任务的框架下，实时精确反馈飞机位置非常重要，最近人们探索了几种替代基于GNSS的方法用于无人机在室内导航中的定位技术。在本文中，我们提出了一种基于低功耗蓝牙（BLE）信标的低成本无人机室内定位系统（IPS），该系统利用接收信号强度指示（RSSI）进行距离估计和定位。测量得到的RSSI值所提供的距离信息可能会因多径、反射和衰落而变差，这些因素会导致RSSI出现不可预测的变化，并可能导致测量质量不佳。为了提高位置估计的准确性，这项工作应用了一种差分距离校正（DDC）技术，类似于差分GNSS（DGNSS）和实时动态（RTK）定位。该方法使用来自位于已知坐标处的参考站的差分信息来校正流动站的位置。建立了一个数学模型来分析RSSI与放置在室内操作区域的蓝牙设备（Eddystone BLE信标）之间的距离关系。主参考站是树莓派4 B型，流动站（未知目标）是安装在无人机上的Arduino Nano 33 BLE微控制器。通过三边测量法实现位置估计，并应用扩展卡尔曼滤波器（EKF），考虑信标信号的非线性特性来校正噪声、漂移和偏差误差产生的数据。实验结果和系统性能分析表明了该方法的可行性，以及通过DCC技术获得的位置不确定性的降低。
