Wi-Fi indoor positioning systems are based on received signal strength indicator (RSSI) measurements and fingerprinting, by matching measured data with the database. Hence, generation of RSSI fingerprint database is essential for a Wi-Fi-based indoor positioning system; this requires significant time and effort. The study utilizes virtual access points to increase the number of access points in an indoor environment without necessitating additional hardware. Increases in the total access points is advantageous because it makes the database more granular, and the Kriging algorithm is introduced to solve the issue with less effort. The study also aims to apply deep learning neural network (DNN) in Wi-Fi fingerprinting using RSSI. The proposed system utilizes the neural network (NN) and Kriging algorithm to perform standard Wi-Fi fingerprinting, without difficulties in generating a fingerprint map. The result of a simple location estimation test led to an accuracy of 97.14%, thereby indicating that the application of NN and Kriging improves indoor localization. Further experiments should be conducted to test the complete effectiveness of the proposed system as compared to other systems employing DNN.

中文翻译：

---

具有增强型虚拟接入点的基于神经网络的室内定位系统

Wi-Fi 室内定位系统基于接收信号强度指标 (RSSI) 测量和指纹识别，通过将测量数据与数据库进行匹配。因此，RSSI 指纹数据库的生成对于基于 Wi-Fi 的室内定位系统是必不可少的；这需要大量的时间和精力。该研究利用虚拟接入点来增加室内环境中接入点的数量，而无需额外的硬件。访问点总数的增加是有利的，因为它使数据库更加细化，并且引入了克里金算法以更轻松地解决问题。该研究还旨在使用 RSSI 将深度学习神经网络 (DNN) 应用于 Wi-Fi 指纹识别。所提出的系统利用神经网络 (NN) 和克里金算法来执行标准的 Wi-Fi 指纹识别，生成指纹图没有困难。简单的位置估计测试结果为 97.14%，表明 NN 和 Kriging 的应用改善了室内定位。与采用 DNN 的其他系统相比，应该进行进一步的实验来测试所提出的系统的完整有效性。