Improved indoor positioning using BLE differential distance correction and pedestrian dead reckoning

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Abstract

Recently, indoor positioning becomes a popular issue because of its corresponding location-aware applications. Owing to the limits of the sheltered signal of satellites in indoor environments, one of the alternative scheme is Bluetooth Low Energy (BLE) technology. BLE device broadcasts Received Signal Strength Indicator (RSSI) for distance estimation and further positioning. However, in the complex indoor environment, the reflection, fading, and multipath effect of BLE make the variable RSSI and may lead to poor quality of RSSI. In this study, the concept called Differential Distance Correction (DDC) is similar to the Differential Global Navigation Satellite System (DGNSS). This method can eliminate some residuals and further improve the results with the corrected distance. On the other hand, Pedestrian Dead Reckoning (PDR) is another common indoor positioning method. PDR can propagate the next position from the current position by the implemented of inertial sensors. Despite that, the error of inertial sensors would accumulate with time and walking distance, which position update is required for restraining the drift. Accordingly, the two indoor positioning methods have their strong and weak point. BLE-based positioning is absolute positioning, while PDR is relative positioning. This study proposes a concept that combines the two methods. The pedestrian receives the RSSI and records the information from inertial sensors simultaneously. Through the complementary of two methods, the positioning results would be improved from 29% to 66% according to different travelled distance.

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APA

Tang, Y. T., Kuo, Y. T., Liao, J. K., & Chiang, K. W. (2020). Improved indoor positioning using BLE differential distance correction and pedestrian dead reckoning. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (Vol. 43, pp. 193–198). International Society for Photogrammetry and Remote Sensing. https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-193-2020

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