This study focuses on improving accuracy of Bluetooth-based indoor positioning system. The distance model correction is proposed and applied in experimental environments. Bluetooth wireless technology is the global wireless standard that exchanges data over short distance between devices. W With the development of wireless technologies, Bluetooth has developed to a new version 4.0 in 2010. The major innovation is called Bluetooth Low Energy (BLE). Its characteristics of low cost, low energy consumption, and interoperability bring about a suitable tool for connecting network between devices. Most of the mobile devices are equipped with Bluetooth functionality that also makes it a good candidate for indoor positioning. Beacon, which is the application of BLE-based technology, is capable of transmitting information and one of them is called Received Signal Strength Index (RSSI) which can be converted to distance depending on the model of signal strength and real distance.

This research utilizes beacon in indoor positioning system. After detecting the RSSI from beacon, the distance between transmitter and receiver can be estimated through a distance model. The unknown position is subsequently calculated by trilateration. However, the signal strength of BLE will be influenced by the surrounding environments, i.e. multipath effect and shelter which make the signal weak and unstable. The feeble signal leads to poor accuracy of estimated distance and positioning result. To improve the performance of the positioning method, this research proposes a novel method, which corrects the distance derived from the model. The proposed differential distance correction is based on the differential which is similar to the principle of Global Positioning System (GPS). In order to obtain better positioning accuracy, it exploits reference station which is a known point to compute the residual of distance so as to correct the distance observation from receiving station. Once the distance of receiving station to each beacon is revised, the positioning result calculated by trilateration will be closer to the real position. This research has some check points in different testing environments. Finally, this study uses Root Mean Square Error (RMSE) and standard deviation to evaluate the accuracy of the check points which the true location is surveyed by the total station. The experimental results show that the positions after distance model correction are more concentrated and closer to real position in terms of overall accuracy.