Bluetooth indoor positioning system based on improved RSSI data

Rongxiang Nie; Nan Xu; Li Cheng; Jiayi Chen

doi:10.1117/12.3033732

13 June 2024 Bluetooth indoor positioning system based on improved RSSI data

Rongxiang Nie, Nan Xu, Li Cheng, Jiayi Chen

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131805I (2024) https://doi.org/10.1117/12.3033732
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

Bluetooth Low Power (BLE) indoor location has gained increasing attention due to its low cost, low power, and ubiquitous availability in mobile devices. In this paper, a new RSSI data labeling method is studied in the offline phase of indoor positioning. In order to achieve better positioning results, two dimensional reduction algorithms such as t-SNE and PCA are used to optimize the data. Then the preprocessed data is used to test the location. Two simple but widely used localization algorithms, weighted centroid and kNN fingerprint recognition, are used to compare the influence of different regions, acquisition equipment and transmitting power on positioning accuracy. In addition, meta-heuristic based optimization techniques were used to determine the optimal TxPower configuration settings for BLE devices. The results reported in this work show an accuracy of nearly 90.7%, a 45% improvement over positioning accuracy using the original RSSI database. This also shows the feasibility of the scheme in developing more accurate indoor positioning system.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Rongxiang Nie, Nan Xu, Li Cheng, and Jiayi Chen "Bluetooth indoor positioning system based on improved RSSI data", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131805I (13 June 2024); https://doi.org/10.1117/12.3033732

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