Multichannel and Multi-RSS Based BLE Range Estimation for Indoor Tracking of Commercial Smartphones

The rapid development of Bluetooth positioning technology is increasingly becoming a research hotspot for indoor positioning. As compared with other indoor positioning technologies, Bluetooth low energy (BLE) received signal strength (RSS)-based positioning solutions have low cost, are easily deployable, and have low power consumption, thus making them a competitive technology in many fields. Like other wireless positioning techniques, BLE signals are susceptible to indoor multipath reflection and nonline-of-sight (NLOS) error. The multichannel differences and device heterogeneity present challenges for BLE range estimation and trilateration. To overcome these limitations, based on the designed multimodule BLE transmitter (MMBT) hardware and associated software, this work develops an indoor positioning solution for consumer smartphones. Based on this hardware, the smartphone senses the current Bluetooth broadcast channel in real time and obtains a fine-grained BLE ranging by using the proposed hybrid channel path loss model (HCPLM) and multi-RSS values. A multilevel constraint fusion localization framework based on a robust adaptive extended Kalman filter (EKF) is built on pervasive smartphone hardware. In addition, a combined BLE ranging measurement/data-driven pedestrian walking velocity/pedestrian accessibility information positioning algorithm is implemented. The performance of the proposed MMBT-based BLE ranging is analyzed and a multilevel measurement quality control strategy (MMQCS) is established to further enhance the positioning performance of the system. The preliminary experiments demonstrate that the proposed BLE solution achieves a positioning accuracy of 0.78 m in a typical indoor environment, e.g., office and corridor.