Sliding Window, Hierarchical Classification, Regression, and Genetic Algorithm for RFID Indoor Positioning Systems

Abstract

Several surveys in the Indoor Positioning System (IPS) use the Radio Signal Strength Indicator (RSSI) to locate target objects. However, RSSI suffers significantly from the multipath phenomenon and other environmental factors such as the absence of the field of view, reflective materials, excessive obstacles, and a high density of objects. To overcome such problems, we propose three new IPS approaches using Radio Frequency Identification (RFID) technology and the collaborative use of Sliding Window, Hierarchical Classification, Random Forest Regressor, and Genetic Algorithm. We tested the new approaches on three datasets obtained in different environments and compared them with two state-of-the-art IPS approaches (Landmarc and SVR-Landmarc). One of the main results got was a precision of 4.82 cm in an environment with 7,000 target tags, reducing by up to 92.91% the average error w.r.t. other IPS approaches presented in the literature, including more complex indoor scenarios.