Cocktail: An Rf-Based Hybrid Approach For Indoor Localization

Traditional RF-based indoor positioning approaches use only Radio Signal Strength Indicator (RSSI) to locate the target object. But RSSI suffers significantly from the multi-path phenomenon and other environmental factors. Hence, the localization accuracy drops dramatically in a large tracking field. To solve this problem, this paper introduces one more resource, the dynamic of RSSI, which is the variance of signal strength caused by the target object and is more robust to environment changes. By combining these two resources, we are able to greatly improve the accuracy and scalability of current RF-based approaches. We call such hybrid approach COCKTAIL. It employs both the technologies of active RFID and Wireless Sensor Networks (WSNs). Sensors use the dynamic of RSSI to figure out a cluster of reference tags as candidates. The final target location is estimated by using the RSSI relationships between the target tag and candidate reference tags. Experiments show that COCKTAIL can reach a remarkable high degree of localization accuracy to 0.45m, which outperforms significantly to most of the pure RF-based localization approaches.