Optimizing indoor localization precision: advancements in RFID strategies for enhanced performance

Abstract RFID technology is being used more and more for complex interior localization, as a result of the quick spread of mobile devices and the smooth incorporation of artificial intelligence (AI). But conventional approaches have severe difficulties with antenna placement and constrained signal feature sizes, which limits their performance. To improve indoor localization and get over these drawbacks, this study presents a novel method that combines the Unscented Kalman Filter (UKF) with Wavelet Neural Networks (WNN). The most significant development is the incorporation of phase data into input vectors along with Received Signal Strength Indication (RSSI), which significantly increases accuracy. Empirical results show notable improvements of 88.9%, 99.2%, 95.2%, and 37.5% in critical metrics like Mean Localization Error (MLE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Localization Error Offset (LEO) in particular. This work highlights the critical role of sophisticated techniques like WNN and UKF, highlighting the importance of phase as a critical component, to improve the performance of RFID-based indoor localization. The results indicate a promising trajectory for future advancements in this field as well.