Clustering and Hierarchical Classification for High-Precision RFID Indoor Location Systems

Object location in indoor environments is challenging when there is no physical contact, a field of view, reflective materials, and an excess of obstacles. Several works propose using Radio Frequency Identification technology (RFID) and machine learning methods to develop location systems in those situations. However, using an object as a target class slows learning and prediction in large-scale environments. To circumvent such problems, we proposed a location system that uses hierarchical classification. We divided the environment into regions to reduce the classifier’s training and the number of predicted classes. To define the regions, we used clustering techniques, indicating which clustering technique achieves the best performance in the proposed scenario. The main contribution of this work is a high-precision location system for large-scale environments. The results showed the proposed system’s implantation in a real environment with 400 target objects with 5 cm of location precision. The accuracy for region detection is 99.36%, while for identifying the object is 99.94%. Additionally, with the proposed hierarchical approach, we showed a reduction of 38.16% and 58.39% in processing time and classifier model size.