A new method for improving Wi-Fi based indoor positioning accuracy

Abstract

Wi-Fi and smartphone based positioning technologies are play-ing a more and more important role in Location Based Service (LBS) indus-tries due to the rapid development of the smartphone market. However, the low positioning accuracy of these technologies is still an issue for indoor positioning. To address this problem, a new method for improving the in-door positioning accuracy was developed. The new method initially used the Nearest Neighbor (NN) algorithm of the fingerprinting method to iden-tify the initial position estimate of the smartphone user. Then two distance correction values in two roughly perpendicular directions were calculated by the pass loss model based on the two signal strength indicator (RSSI) values observed. The errors from the path loss model were eliminated through differencing two model-derived distances from the same access point. The new method was tested and the results were compared and as-sessed against that of the commercial Ekahau RTLS system and the NN algorithm. The preliminary results showed that the positioning accuracy has been improved consistently after the new method was applied and the root mean square accuracy was improved to 3.4 m from 3.8 m of the NN algorithm