Indoor positioning with trilateration using orientation-based FSPL model selection
By: Dominik Csík, Ákos Odry and Peter Sarcevic
References
- C. Becker Westphall, I. Pozniak-Koszalka, E. Borcoci, D. Krstic, and International Academy, Research, and Industry Association, Eds., ICWMC 2015: the Eleventh International Conference on Wireless and Mobile Communications: October 11-16, 2015, St. Julians, Malta. Red Hook, NY: Curran Associates, Inc, 2015.
- B. Yang, L. Guo, R. Guo, M. Zhao, and T. Zhao, “A Novel Trilateration Algorithm for RSSI-Based Indoor Localization,” IEEE Sensors J., vol. 20, no. 14, pp. 8164–8172, Jul. 2020, doi: 10.1109/JSEN.2020.2980966.
- M. E. Rusli, M. Ali, N. Jamil, and M. M. Din, “An Improved Indoor Positioning Algorithm Based on RSSI-Trilateration Technique for Internet of Things (IOT),” in 2016 International Conference on Computer and Communication Engineering (ICCCE), Kuala Lumpur, Malaysia: IEEE, Jul. 2016, pp. 72–77. doi: 10.1109/ICCCE.2016.28.
- B. Zhou, Z. Wu, Z. Chen, X. Liu, and Q. Li, “Wi-Fi RTT/Encoder/INS-Based Robot Indoor Localization Using Smartphones,” IEEE Trans. Veh. Technol., vol. 72, no. 5, pp. 6683–6694, May 2023, doi: 10.1109/TVT.2023.3234283.
- D. P. Kun, E. Baksáné Varga, and Z. Tóth, “Ontology based Indoor Navigation Service for the ILONA System,” Infocommunications journal, no. 3, pp. 20–29, 2018, doi: 10.36244/ICJ.2018.3.4.
- X. Liu, L. Chen, Z. Jiao, and X. Lu, “Robust Pedestrian Dead Reckoning Integrating Magnetic Field Signals and Digital Terrestrial Multimedia Broadcasting Signals,” Remote Sensing, vol. 15, no. 13, p. 3229, Jun. 2023, doi: 10.3390/rs15133229.
- I. Sharp and K. Yu, “Sensor-based dead-reckoning for indoor positioning,” Physical Communication, vol. 13, pp. 4–16, Dec. 2014, doi: 10.1016/j.phycom.2013.11.013.
- V. Cantón Paterna, A. Calveras Augé, J. Paradells Aspas, and M. Pérez Bullones, “A Bluetooth Low Energy Indoor Positioning System with Channel Diversity, Weighted Trilateration and Kalman Filtering,” Sensors, vol. 17, no. 12, p. 2927, Dec. 2017, doi: 10.3390/s17122927.
- R. Bembenik and K. Falcman, “BLE Indoor Positioning System Using RSSI-based Trilateration,” Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, vol. 11, no. 3, pp. 50–69, 0 2020, doi: 10.22667/JOWUA.2020.09.30.050.
- C. Gentner, M. Ulmschneider, I. Kuehner, and A. Dammann, “WiFi-RTT Indoor Positioning,” in 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, OR, USA: IEEE, Apr. 2020, pp. 1029–1035. doi: 10.1109/PLANS46316.2020.9110232.
- Y. Li, Z. Ghassemlooy, X. Tang, B. Lin, and Y. Zhang, “A VLC Smartphone Camera Based Indoor Positioning System,” IEEE Photon. Technol. Lett., vol. 30, no. 13, pp. 1171–1174, Jul. 2018, doi: 10.1109/LPT.2018.2834930.
- S. Sharma, D. Kumar, and K. Kishore, “Wireless Sensor Networks - A Review on Topologies and Node Architecture,” Int. J. Comp. Sci. Eng., vol. 1, no. 2, pp. 19–25, Oct. 2013.
- Z. Xiong, Z. Y. Song, A. Scalera, F. Sottile, R. Tomasi, and M. A. Spirito, “Enhancing WSN-Based Indoor Positioning and Tracking through RFID Technology,” in 2012 Fourth International EURASIP Workshop on RFID Technology, Torino, Italy: IEEE, Sep. 2012, pp. 107–114. doi: 10.1109/RFID.2012.26.
- H. Guo, H. Li, J. Xiong, and M. Yu, “Indoor positioning system based on particle swarm optimization algorithm,” Measurement, vol. 134, pp. 908–913, Feb. 2019, doi: 10.1016/j.measurement.2018.12.038.
- S. Sung, H. Kim, and J.-I. Jung, “Accurate Indoor Positioning for UWB-Based Personal Devices Using Deep Learning,” IEEE Access, vol. 11, pp. 20095–20113, 2023, doi: 10.1109/ACCESS.2023.3250180.
- A. Alitaleshi, H. Jazayeriy, and J. Kazemitabar, “EA-CNN: A smart indoor 3D positioning scheme based on Wi-Fi fingerprinting and deep learning,” Engineering Applications of Artificial Intelligence, vol. 117, p. 105509, Jan. 2023, doi: 10.1016/j.engappai.2022.105509.
- J. Bi et al., “PSOSVRPos: WiFi indoor positioning using SVR optimized by PSO,” Expert Systems with Applications, vol. 222, p. 119778, Jul. 2023, doi: 10.1016/j.eswa.2023.119778.
- A. Strzoda, R. Marjasz, and K. Grochla, “LoRa Positioning in Verification of Location Data’s Credibility,” Infocommunications journal, vol. 14, no. 4, pp. 56–61, 2022, doi: 10.36244/ICJ.2022.4.8.
- P. Wu, S. Su, Z. Zuo, X. Guo, B. Sun, and X. Wen, “Time Difference of Arrival (TDoA) Localization Combining Weighted Least Squares and Firefly Algorithm,” Sensors, vol. 19, no. 11, p. 2554, Jun. 2019, doi: 10.3390/s19112554.
- Y. Wu, C. Zhang, L. Tong, and X. Shi, “Location Optimization Based on Improved 3D DV-HOP Algorithm in Wireless Sensor Networks,” IEEE Access, vol. 11, pp. 85525–85536, 2023, doi: 10.1109/ACCESS.2023.3303569.
- A. O. De Sá, N. Nedjah, and L. De Macedo Mourelle, “Distributed efficient localization in swarm robotic systems using swarm intelligence algorithms,” Neurocomputing, vol. 172, pp. 322–336, Jan. 2016, doi: 10.1016/j.neucom.2015.03.099.
- D. Li and Z. Niu, “A Wireless Fingerprint Positioning Method Based on Wavelet Transform and Deep Learning,” IJGI, vol. 10, no. 7, p. 442, Jun. 2021, doi: 10.3390/ijgi10070442.
- R. Safwat, E. Shaaban, S. M. Al-Tabbakh, and K. Emara, “Fingerprint-based indoor positioning system using BLE: real deployment study,” Bulletin EEI, vol. 12, no. 1, pp. 240–249, Feb. 2023, doi: 10.11591/eei.v12i1.3798.
- Z. Turgut and A. G. Kakisim, “An explainable hybrid deep learning architecture for WiFi-based indoor localization in Internet of Things environment,” Future Generation Computer Systems, vol. 151, pp. 196–213, Feb. 2024, doi: 10.1016/j.future.2023.10.003.
- L. Rissmann, J. Wittmann, C. Huber, and S. Meissner, “Application of Machine Learning for 3D Localization using RFID-Tag RSSI Values in Logistics Environments,” in Proceedings of the 26th International Conference on Harbor, Maritime and Multimodal Logistic Modeling & Simulation, CAL-TEK srl, 2024. doi: 10.46354/i3m.2024.hms.004.
- C. Agualimpia-Arriaga, S. Govindasamy, B. Soni, C.-I. Páez-Rueda, and A. Fajardo, “RSSI-Based Indoor Localization Using Machine Learning for Wireless Sensor Networks: A Recent Review,” in 2024 IEEE ANDESCON, Cusco, Peru: IEEE, Sep. 2024, pp. 1–6. doi: 10.1109/ANDESCON61840.2024.10755908.
- Y. Zhu, X. Luo, S. Guan, and Z. Wang, “Indoor Positioning Method Based on WiFi/Bluetooth and PDR Fusion Positioning,” in 2021 13th International Conference on Advanced Computational Intelligence (ICACI), Wanzhou, China: IEEE, May 2021, pp. 233–238. doi: 10.1109/ICACI52617.2021.9435887.
- Z. Liu, Z. Li, A. Liu, K. Shao, Q. Guo, and C. Wang, “LVI-Fusion: A Robust Lidar-Visual-Inertial SLAM Scheme,” Remote Sensing, vol. 16, no. 9, p. 1524, Apr. 2024, doi: 10.3390/rs16091524.
- A. Poulose, O. S. Eyobu, and D. S. Han, “A Combined PDR and Wi-Fi Trilateration Algorithm for Indoor Localization,” in 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Okinawa, Japan: IEEE, Feb. 2019, pp. 072–077. doi: 10.1109/ICAIIC.2019.8669059.
- D. Csik, A. Odry, R. Pesti, J. Sarosi, and P. Sarcevic, “A novel orientation-based FSPL model parameter optimization method using PSO for indoor localization,” in 2023 IEEE 21st World Symposium on Applied Machine Intelligence and Informatics (SAMI), Herl’any, Slovakia: IEEE, Jan. 2023, pp. 000201–000206. doi: 10.1109/SAMI58000.2023.10044514.
- H. T. Friis, “A Note on a Simple Transmission Formula,” Proc. IRE, vol. 34, no. 5, pp. 254–256, May 1946, doi: 10.1109/JRPROC.1946.234568.
- “Recommendation ITU-R P.1238-12 (08/2023) Propagation data and prediction methods for the planning of indoor radiocommunication systems and radio local area networks in the frequency range 300 MHz to 450 GHz”.
- M. N. Rahman, M. T. Ir. A. T. Hanuranto, and S. T. M. T. R. Mayasari, “Trilateration and iterative multilateration algorithm for localization schemes on Wireless Sensor Network,” in 2017 International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC), Yogyakarta, Indonesia: IEEE, Sep. 2017, pp. 88–92. doi: 10.1109/ICCEREC.2017.8226710.
- Y. Cao, H. Kandula, and X. Li, “Measurement and Analysis of RSS Using Bluetooth Mesh Network for Localization Applications,” Network, vol. 1, no. 3, pp. 315–334, Dec. 2021, doi: 10.3390/network1030018.
- D. Csík, Á. Odry, R. Pesti, and P. Sarcevic, “Impact of antenna orientation on localization accuracy using RSSI-based Trilateration,” Analecta, vol. 18, no. 2, 2024.
Language: English
Page range: 357 - 365
Submitted on: May 12, 2026
Published on: Jun 17, 2026
Published by: Slovak University of Technology in Bratislava
In partnership with: Paradigm Publishing Services
Publication frequency: 6 issues per year
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© 2026 Dominik Csík, Ákos Odry, Peter Sarcevic, published by Slovak University of Technology in Bratislava
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.