Department of Electrical Engineering and Graduate Institute of Communication Engineering, National Taiwan University, Taipei 10617, Taiwan.
Sensors (Basel). 2023 Jan 26;23(3):1376. doi: 10.3390/s23031376.
This study presents a Wi-Fi-based passive indoor positioning system (IPS) that does not require active collaboration from the user or additional interfaces on the device-under-test (DUT). To maximise the accuracy of the IPS, the optimal deployment of Wi-Fi Sniffers in the area of interest is crucial. A modified Genetic Algorithm (GA) with an entropy-enhanced objective function is proposed to optimize the deployment. These Wi-Fi Sniffers are used to scan and collect the DUT's Wi-Fi received signal strength indicators (RSSIs) as Wi-Fi fingerprints, which are then mapped to reference points (RPs) in the physical world. The positioning algorithm utilises a weighted k-nearest neighbourhood (WKNN) method. Automated data collection of RSSI on each RP is achieved using a surveying robot for the Wi-Fi 2.4 GHz and 5 GHz bands. The preliminary results show that using only 20 Wi-Fi Sniffers as features for model training, the offline positioning accuracy is 2.2 m in terms of root mean squared error (RMSE). A proof-of-concept real-time online passive IPS is implemented to show that it is possible to detect the online presence of DUTs and obtain their RSSIs as online fingerprints to estimate their position.
本研究提出了一种基于 Wi-Fi 的被动室内定位系统 (IPS)，它不需要用户主动协作或在被测设备 (DUT) 上添加额外的接口。为了最大限度地提高 IPS 的准确性，在感兴趣的区域中优化 Wi-Fi 嗅探器的部署至关重要。提出了一种具有熵增强目标函数的改进遗传算法 (GA) 来优化部署。这些 Wi-Fi 嗅探器用于扫描和收集 DUT 的 Wi-Fi 接收信号强度指示器 (RSSI) 作为 Wi-Fi 指纹，然后将其映射到物理世界中的参考点 (RP)。定位算法利用加权 k-最近邻 (WKNN) 方法。使用测量机器人自动收集每个 RP 上的 RSSI，用于 Wi-Fi 2.4 GHz 和 5 GHz 频段。初步结果表明，仅使用 20 个 Wi-Fi 嗅探器作为模型训练的特征，离线定位精度在均方根误差 (RMSE) 方面为 2.2 米。实现了一个概念验证实时在线被动 IPS，以表明可以检测 DUT 的在线存在，并获取其 RSSI 作为在线指纹来估计其位置。
