Abstract
Indoor positioning using Bluetooth Low Energy (BLE) beacons has attracted considerable attention after the release of the BLE protocol. A number of efforts have been exerted to improve the performance of BLE-based indoor positioning. However, few studies pay attention to the BLE-based indoor positioning in a dense Bluetooth environment, where the propagation of BLE signals become more complex and more fluctuant. In this paper, we draw attention to the problems resulting from the dense Bluetooth environment, and it turns out that the dense Bluetooth environment would result in a high received signal strength indication (RSSI) variation and a longtime interval collection of BLE. Hence, to mitigate the effects of the dense Bluetooth environment, we propose a hybrid method fusing sliding-window filtering, trilateration, dead reckoning and the Kalman filtering method to improve the performance of the BLE indoor positioning. The Kalman filter is exploited to merge the trilateration and dead reckoning. Extensive experiments in a real implementation are conducted to examine the performance of three approaches: trilateration, dead reckoning and the fusion method. The implementation results proved that the fusion method was the most effective method to improve the positioning accuracy and timeliness in a dense Bluetooth environment. The positioning root-mean-square error (RMSE) calculation results have showed that the hybrid method can achieve a real-time positioning and reduce error of indoor positioning.