Karakusak Muhammed Zahid, Kivrak Hasan, Watson Simon, Ozdemir Mehmet Kemal
Graduate School of Engineering and Natural Sciences, Istanbul Medipol University, 34810 Istanbul, Turkey.
Department of Electronics Technology, Karabuk University, 78010 Karabuk, Turkey.
Sensors (Basel). 2023 Dec 18;23(24):9903. doi: 10.3390/s23249903.
In recent decades, there have been significant research efforts focusing on wireless indoor localization systems, with fingerprinting techniques based on received signal strength leading the way. The majority of the suggested approaches require challenging and laborious Wi-Fi site surveys to construct a radio map, which is then utilized to match radio signatures with particular locations. In this paper, a novel next-generation cyber-physical wireless indoor positioning system is presented that addresses the challenges of fingerprinting techniques associated with data collection. The proposed approach not only facilitates an interactive digital representation that fosters informed decision-making through a digital twin interface but also ensures adaptability to new scenarios, scalability, and suitability for large environments and evolving conditions during the process of constructing the radio map. Additionally, it reduces the labor cost and laborious data collection process while helping to increase the efficiency of fingerprint-based positioning methods through accurate ground-truth data collection. This is also convenient for working in remote environments to improve human safety in locations where human access is limited or hazardous and to address issues related to radio map obsolescence. The feasibility of the cyber-physical system design is successfully verified and evaluated with real-world experiments in which a ground robot is utilized to obtain a radio map autonomously in real-time in a challenging environment through an informed decision process. With the proposed setup, the results demonstrate the success of RSSI-based indoor positioning using deep learning models, including MLP, LSTM Model 1, and LSTM Model 2, achieving an average localization error of ≤2.16 m in individual areas. Specifically, LSTM Model 2 achieves an average localization error as low as 1.55 m and 1.97 m with 83.33% and 81.05% of the errors within 2 m for individual and combined areas, respectively. These outcomes demonstrate that the proposed cyber-physical wireless indoor positioning approach, which is based on the application of dynamic Wi-Fi RSS surveying through human feedback using autonomous mobile robots, effectively leverages the precision of deep learning models, resulting in localization performance comparable to the literature. Furthermore, they highlight its potential for suitability for deployment in real-world scenarios and practical applicability.
近几十年来，人们对无线室内定位系统进行了大量的研究工作，其中基于接收信号强度的指纹识别技术处于领先地位。大多数建议的方法需要进行具有挑战性且费力的Wi-Fi现场勘测来构建无线电地图，然后利用该地图将无线电签名与特定位置进行匹配。本文提出了一种新型的下一代信息物理无线室内定位系统，该系统解决了与数据收集相关的指纹识别技术的挑战。所提出的方法不仅促进了交互式数字表示，通过数字孪生接口促进明智的决策，而且在构建无线电地图的过程中确保了对新场景的适应性、可扩展性以及对大型环境和不断变化的条件的适用性。此外，它降低了劳动力成本和繁琐的数据收集过程，同时通过准确的地面真值数据收集有助于提高基于指纹的定位方法的效率。这对于在远程环境中工作也很方便，以提高人类在进入受限或危险地点时的安全性，并解决与无线电地图过时相关的问题。通过实际实验成功验证和评估了信息物理系统设计的可行性，在实际实验中，地面机器人被用于在具有挑战性的环境中通过明智的决策过程实时自主获取无线电地图。在所提出的设置下，结果表明使用深度学习模型（包括MLP、LSTM模型1和LSTM模型2）进行基于RSSI的室内定位取得了成功，在各个区域实现了≤2.16米的平均定位误差。具体而言，LSTM模型2在单个区域和组合区域分别实现了低至1.55米和1.97米的平均定位误差，误差在2米以内的分别为83.33%和81.05%。这些结果表明，所提出的基于通过自主移动机器人利用人类反馈进行动态Wi-Fi RSS测量应用的信息物理无线室内定位方法，有效地利用了深度学习模型的精度，从而实现了与文献相当的定位性能。此外，它们突出了其在实际场景中部署的适用性和实际应用潜力。
