Improved RSSI-based path-loss model for indoor positioning and navigation in LabVIEW using trilateration
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Abstract
Indoor positioning and navigation now contribute in many applications to track and direct people inside the building. The popular trilateration technique is utilized to detect user’s position through three access point of Bluetooth low energy. However, received signal from Bluetooth has insignificancy due to the noise, multipath, fading or other radio propagation. A study of received signal characteristics in specific indoor locations must be considered to predict and improve the accuracy of estimation. In this case, the adjustment of raw received signal readings is essential. we extracted linear regression model by compare between raw and analytical value of received signal power. Then, utilizing the corrected received signal, finding the best suitable path loss exponent model is required in order to minimize position estimation error. The last step is applying the additional model and the chosen path-loss on LabVIEW as a mean to visualize position and navigation system. The result yield that the new model gives lower error on 2 out of 3 access points. The corresponding path loss exponent n = 2.1 is selected to comply with the indoor environment in this case. The lowest RMSE yields 1.24 and considered as a good level of accuracy. The Navigation system worked well providing route to the desired location in the Laboratory.
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[1]
D. Farahiyah and A. F. Reza, “Improved RSSI-based path-loss model for indoor positioning and navigation in LabVIEW using trilateration”, INFOTEL, vol. 13, no. 3, pp. 151-159, Aug. 2021.
Section
Telecommunication
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[3] J. Hoa and B. Soewito, “Monitoring Human Movement in Building Using Bluetooth Low Energy,” CommIT (Communication Inf. Technol. J., vol. 12, no. 2, 2018, doi: 10.21512/commit.v12i2.4963.
[4] O. Toutian Esfahani and A. Jahangir Moshayedi, “Accuracy of the Positioning Systems for the Tracking of Alzheimer’s Patients - A Review,” Int. J. Appl. Electron. Phys. Robot., vol. 2, no. 2, 2015, doi: 10.7575/aiac.ijaepr.v.2n.2p.10.
[5] F. Zafari, A. Gkelias, and K. K. Leung, “A Survey of Indoor Localization Systems and Technologies,” IEEE Commun. Surv. Tutorials, vol. 21, no. 3, 2019, doi: 10.1109/COMST.2019.2911558.
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[18] J. Hu, D. Liu, Z. Yan, and H. Liu, “Experimental Analysis on Weight K-Nearest Neighbor Indoor Fingerprint Positioning,” IEEE Internet Things J., vol. 6, no. 1, 2019, doi: 10.1109/JIOT.2018.2864607.
[19] B. Yang, L. Guo, R. Guo, M. Zhao, and T. Zhao, “A Novel Trilateration Algorithm for RSSI-Based Indoor Localization,” IEEE Sens. J., vol. 20, no. 14, 2020, doi: 10.1109/JSEN.2020.2980966.
[20] H. J. Jo and S. Kim, “Indoor smartphone localization based on LOS and NLOS identification,” Sensors (Switzerland), vol. 18, no. 11, 2018, doi: 10.3390/s18113987.
[21] D. E. Grzechca, P. Pelczar, and L. Chruszczyk, “Analysis of Object Location Accuracy for iBeacon Technology based on the RSSI Path Loss Model and Fingerprint Map,” Int. J. Electron. Telecommun., vol. 62, no. 4, pp. 371–378, Dec. 2016, doi: 10.1515/eletel-2016-0051.
[22] J. Bi et al., “Fast radio map construction by using adaptive path loss model interpolation in large-scale building,” Sensors (Switzerland), vol. 19, no. 3, Feb. 2019, doi: 10.3390/s19030712.
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[24] C. Yang and H. R. Shao, “WiFi-based indoor positioning,” IEEE Commun. Mag., vol. 53, no. 3, 2015, doi: 10.1109/MCOM.2015.7060497.