AI-Based Indoor Localization Using Virtual Anchors in Combination with Wake-Up Receiver Nodes

Accurate indoor localization is essential for navigation, monitoring, and industrial applications, especially in environments with Non-line of sight (NLOS) conditions. An indoor positioning system consists of fixed physical nodes, referred to as anchors, which serve as reference nodes with known locations, and entities that could be persons or objects that are also equipped with a node, referred to as targets, whose positions are estimated based on signal measurements exchanged with the surrounding anchors. Although RSSI is widely used due to hardware simplicity, its performance is often affected by signal degradation, multipath propagation, and environmental interference. To address this limitation, this work aims to develop an indoor positioning system, especially in wide areas with a minimal number of physical anchors, while maintaining high positioning accuracy and low latency. The proposed approach integrates VA, RSSI-based multilateration, and ML as a tool to refine and improve positioning accuracy, where ML models are used to predict the VA features and subsequently predict the corresponding distances. In addition, the system relies on energy-efficient WuRx nodes, which ensure a low power consumption and support on-demand communication. The study area covers two distinct floors with a total area of 366.9 m², covered using only four physical anchors. Two studies were performed, the offline and the online, in order to evaluate the proposed system under both the theoretical performance and real implementation conditions. In the offline phase, hexagonal and rectangular grid architectures were compared using multiple machine learning models under varying numbers of virtual anchors. By comparing different architectures and machine learning models, the rectangular grid with 10 virtual anchors combined with the XGBoost model achieved the best performance, resulting in an RMSE of $1.49\mathrm{m}$ with a processing time of approximately $0.15\mathrm{s}$. The online evaluation confirmed the performance of the proposed system, achieving an RMSE of $2.48\mathrm{m}$.