Optimization of RSSI-based Zigbee Indoor Localization System for Determining Distances Between Unknown Nodes

Abstract

An indoor localization system is developed through Zigbee wireless modules by acquiring RSSI values from fixed nodes and processing them through 3D trilateration algorithm to locate multiple unknown nodes, and subsequently, determine distances between them. Kalman filtering is applied for refining RSSI values for localization. The experimental setup focuses on optimization of antenna orientation and reader altitude as well as calibration of signal propagation loss constants for the log-distance path loss model to best characterize the indoor test environment. Optimal results were obtained when both reader's and tag's antennas were positioned perpendicular to the ground and reader is placed at higher altitudes. In testing the accuracy of the indoor localization system, three reader altitude setups were done, specifically 1.5m, 2m, and 2.5m, to determine how it can affect the system. The results of the study show a significant improvement in localization accuracy when the readers were placed at 2.5m. Overall, the localization error was lessened to an averaged value of 0.16m from 0.6389m and 0.35m when the readers were placed at the highest optimal altitude. The accuracy in determining distances between tags was found to significantly increase as distance between the tags increased. From a range of error of 3.23% to 32.02% for 0.5m distance, it decreased to 0.82% to 9.29% for distances 1.5m and above. The findings of this study exhibit positive outputs in using an RSSI-based Zigbee indoor localization system for locating multiple stationary tags and determining distances of tags that are farther apart.