Formation of a database of auxiliary information for positioning in an environment with heterogeneous radio transparency

Objectives. A pressing problem for indoor positioning systems in the absence of access to global navigation satellite systems is low positioning accuracy. This is usually associated with uneven coverage of the work area due to its geometric features or the presence of massive obstacles and walls within its boundaries. This problem is frequently resolved by placing an excessive number of positioning system base stations in the work area. This approach generates a high cost for such systems, which in turn prevents their deployment. Therefore, research and development aimed at improving the accuracy of indoor positioning systems using a minimum number of stations is of great relevance. The author previously proposed a method of increasing the accuracy of indoor positioning by taking into account obstacles known at the design stage of the system. Consideration of such obstacles in calculating the location is achieved through the mechanism of preliminary splitting of radio beacons into groups, and the allocation of reference stations of these groups among the base stations. The aim of the work is to improve this algorithm by automating the stage of preparing information about the grouping of stations.

Methods. A computer simulation method was used, in order to confirm the operability of the algorithm to divide the stations of the positioning system into overlapping groups.

Results. The criteria for automatic station grouping and a universal algorithm for dividing stations into groups were developed, enabling the automated preparation of the minimum necessary initial data for a program implementing an algorithm for positioning in a zone of heterogeneous radio transparency.

Conclusions. Modeling of the proposed algorithm has confirmed its operability. The results obtained can be used as a significant addition to the previously proposed algorithm for taking into account obstacles when calculating distances to base stations.

1. Ninh D.B., He J., Trung V.T., Huy D.P. An effective random statistical method for Indoor Positioning System using WiFi fingerprinting. Future Gener. Comput. Syst. 2020;109:238–248. https://doi.org/10.1016/j.future.2020.03.043

2. Qin F., Zuo T., Wang X. Ccpos: Wifi fingerprint indoor positioning system based on CDAE-CNN. Sensors. 2021;21(4):1114. https://doi.org/10.3390/s21041114

3. Bellavista-Parent V., Torres-Sospedra J., Perez-Navarro A. New trends in indoor positioning based on WiFi and machine learning: A systematic review. In: 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE; 2021. https://doi.org/10.1109/IPIN51156.2021.9662521

4. Bai L., Ciravegna F., Bond R., Mulvenna M. A low cost indoor positioning system using bluetooth low energy. IEEE Access. 2020;8:136858–136871. http://dx.doi.org/10.1109/ACCESS.2020.3012342

5. Essa E., Abdullah B.A., Wahba A. Improve performance of indoor positioning system using BLE. In: 2019 14th International Conference on Computer Engineering and Systems (ICCES). IEEE; 2019. Р. 234–237. https://doi.org/10.1109/ICCES48960.2019.9068142

6. Spachos P., Plataniotis K.N. BLE beacons for indoor positioning at an interactive IoT-based smart museum. IEEE Syst. J. 2020;14(3):3483–3493. https://doi.org/10.1109/JSYST.2020.2969088

7. Dong Z.Y., Xu W.M., Zhuang H. Research on ZigBee indoor technology positioning based on RSSI. Procedia Comput. Sci. 2019;154:424–429. https://doi.org/10.1016/j.procs.2019.06.060

8. Ainul R.D. An enhanced trilateration algorithm for indoor RSSI based positioning system using ZigBee protocol. J. INFOTEL (Informatics, Telecommunication, and Electronics). 2022;14(4):301–306. https://doi.org/10.20895/infotel.v14i4.822

9. Cheng C.H., Syu S.J. Improving area positioning in ZigBee sensor networks using neural network algorithm. Microsyst. Technol. 2021;27(1):1419–1428. https://doi.org/10.1007/s00542-019-04309-2

10. Tian D., Xiang Q. Research on indoor positioning system based on UWB technology. In: 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC). IEEE; 2020. Р. 662–665. https://doi.org/10.1109/ITOEC49072.2020.9141707

11. Li B., Zhao K., Sandoval E.B. A UWB-based indoor positioning system employing neural networks. J. Geovis. Spat. Anal. 2020;4(2):18. https://doi.org/10.1007/s41651-020-00059-2

12. Che F., Ahmed Q.Z., Fontaine J., et al. Feature-based generalized Gaussian distribution method for NLoS detection in ultra-wideband (UWB) indoor positioning system. IEEE Sensors J. 2022;22(19):18726–18739. https://doi.org/10.1109/JSEN.2022.3198680

13. Lopes S.I., Vieira J.M.N., Reis J., Albuquerque D., Carvalho N.B. Accurate smartphone indoor positioning using a WSN infrastructure and non-invasive audio for TDoA estimation. Pervasive and Mobile Computing. 2015;20:29–46. https://doi.org/10.1016/j.pmcj.2014.09.003

14. Zourmand A., Sheng N.W., Hing A.L.K., AbdulRehman M. Human Counting and Indoor Positioning System Using WiFi Technology. In: 2018 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS). IEEE: 2018. https://doi.org/10.1109/I2CACIS.2018.8603690

15. Zhao M., Chang T., Arun A., Ayyalasomayajula R., Zhang C., Bharadia D. ULoc: Low-Power, Scalable and cm-Accurate UWB-Tag Localization and Tracking for Indoor Applications. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 2021;5(3):1–31. https://doi.org/10.1145/3478124

16. Renault S., Akbar Sheikh-Akbari, Ofoegbu E. Low Cost and Energy Efficient Hybrid Wireless Positioning System Using Wi-Fi and Bluetooth Technologies for Wearable Devices. Preprints. 2023. 2023092061. http://doi.org/10.20944/preprints202309.2061.v1

17. Астафьев А.В., Титов Д.В., Жизняков А.Л., Демидов А.А. Метод позиционирования мобильного устройства с использованием сенсорной сети BLE-маяков, аппроксимации значений уровней сигналов RSSI и искусственных нейронных сетей. Компьютерная оптика. 2021;45(2):277–285. https://doi.org/10.18287/2412-6179-CO-826 [Astafiev A.V., Titov D.V., Zhiznyakov A.L., Demidov A.A. A method for mobile device positioning using a sensor network of BLE beacons, approximation of the RSSI value and artificial neural networks. Komp’yuternaya optika = Computer Optics. 2021;45(2):277–285 (in Russ.). https://doi.org/10.18287/2412-6179-CO-826 ]

18. Subedi S., Pyun J.-Y. Practical fingerprinting localization for indoor positioning system by using beacons. J. Sensors. 2017;2017:9742170. https://doi.org/10.1155/2017/9742170

19. Крижановский М.Н. Моделирование алгоритма учета препятствий при расчете локации в системах ближнего позиционирования. Вопросы электромеханики. Труды ВНИИЭМ. 2023;192(1):14–20. https://www.elibrary.ru/evdpoc, URL: https://jurnal.vniiem.ru/text/192/14-20.pdf [Krizhanovskiy M. Modeling of the algorithm for taking into account obstacles in the calculation of location in shortrange positioning systems. Voprosy elektromekhaniki. Trudy VNIIEM = Electromechanical Matters. VNIIEM Studies. 2023;192(1):14–20 (in Russ.). https://www.elibrary.ru/evdpoc, URL: https://jurnal.vniiem.ru/text/192/14-20.pdf ]

20. Obeidat H.A., Khan R., Obeidat O.A., et al. An indoor path loss prediction model using wall correction factors for WLAN and 5G indoor networks. Radio Science J. 2018;53(4):544–564. https://doi.org/10.1002/2018RS006536