Authors:
Hena Kausar,Suvendu Chattaraj,Abhishek Majumdar,DOI NO:
https://doi.org/10.26782/jmcms.2026.02.00008Keywords:
Linear Interpolation,Indoor navigation,Wi-Fi Access Points,Intermittent measurement,Kalman filter,Abstract
Multilateration is a popular geometrical algorithm to determine the location of a mobile smartphone in an indoor environment. In this method, the distance of the smartphone from three or more WiFi access sites is calculated based on the strengths of radio signals. Intermittent measurements of radio signals due to the presence of obstacles in the indoor environment affect the overall localization accuracy. The present work addresses this problem and manages the intermittent measurements issue with an innovative Kalman filter-based approach. The linear interpolation method is applied to obtain uninterrupted coordinate information from WiFi RSS measurements. A Kalman filter is designed that uses these interpolated measurements along with its own sensor data to obtain an optimal localization estimate. Less than 2 meters of final position estimation accuracy is attained in Monte-Carlo simulations, which is better than other state-of-the-art techniques in this domain. Additionally, the performance of this intended approach has been found indistinguishable during frequent loss of measurements, in case of which the conventional trilateration approach could not succeed.Refference:
I. Alfakih, Marwan, Mokhtar Keche, Hadjira Be-noudnine, and Abdelkrim Meche. “Improved Gaussian Mixture Modelling for Accurate Wi-Fi Based Indoor Localization Systems.” Physical Communication, vol. 43, 2020, p. 101218
II. Chen, Jian, Gang Ou, Ao Peng, Lingxiang Zheng, and Jianghong Shi. “An INS/WiFi Indoor Localization System Based on the Weighted Least Squares.” Sensors, vol. 18, no. 5, 2018. 10.3390/s18051458.
III. Golenbiewski, Jaren, and Girma Tewolde. “Wi-Fi Based Indoor Positioning and Navigation System (GPS/INS).” Proceedings of the IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), 2020, pp. 1–7.
IV. Hayward, S. J., Katherine van Lopik, Christopher Hinde, and Andrew A. West. “A Survey of Indoor Location Technologies, Techniques and Applications in Industry.” Internet of Things, vol. 20, 2022, p. 100608.
V. Jan, Shau-Shiun, Shuo-Ju Yeh, and Ya-Wen Liu. “Received Signal Strength Database Interpolation by Kriging for a Wi-Fi Indoor Positioning System.” Sensors, vol. 15, no. 9, 2015, pp. 21377–21393. 10.3390/s150921377.
VI. Kausar, H., and S. Chattaraj. “On Some Issues in Kalman Filter Based Trilateration Algorithms for Indoor Localization Problem.” Proceedings of the IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), vol. 1, 2022, pp. 431–435. 10.1109/SPICES52834.2022.9774037.
VII. Khokhar, Zulqarnain, and Murtaza A. Siddiqi. “Machine Learning Based Indoor Localization Using Wi-Fi and Smartphone.” Journal of Independent Studies and Research – Computing, vol. 18, no. 1, 2021.
VIII. Koweerawong, Chavalit, Komwut Wipusitwarakun, and Kamol Kaemarungsi. “Indoor Localization Improvement via Adaptive RSS Fingerprinting Database.” Proceedings of the International Conference on Information Networking (ICOIN), 2013, pp. 412–416. 10.1109/ICOIN.2013.6496414.
IX. Kuemper, Daniel, Thorben Iggena, Ralf Toenjes, and Elke Pulvermueller. “Valid.IOT: A Framework for Sensor Data Quality Analysis and Interpolation.” Proceedings of the 9th ACM Multimedia Systems Conference, 2018, pp. 294–303.
X. McCool, Danielle, Peter Lugtig, and Barry Schouten. “Maximum Interpolable Gap Length in Missing Smartphone-Based GPS Mobility Data.” Transportation, vol. 51, no. 1, 2024, pp. 297–327.
XI. Navada, Bhagya, and Santhosh Venkata. “Filter Design Using Data Fusion for a Pneumatic Control Valve.” Serbian Journal of Electrical Engineering, vol. 18, 2021, pp. 49–61. 10.2298/SJEE2101049N.
XII. Poulose, Alwin, Odongo Steven Eyobu, and Dong Seog Han. “A Combined PDR and Wi-Fi Trilateration Algorithm for Indoor Localization.” Proceedings of the International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 2019, pp. 072–077. 10.1109/ICAIIC.2019.8669059.
XIII. Redži?, Milan D., Conor Brennan, and Noel E. O’Connor. “SEAMLOC: Seamless Indoor Localization Based on Reduced Number of Calibration Points.” IEEE Transactions on Mobile Computing, vol. 13, no. 6, 2014, pp. 1326–1337. 10.1109/TMC.2013.107.
XIV. Singh, Navneet, Sangho Choe, and Rajiv Punmiya. “Machine Learning Based Indoor Localization Using Wi-Fi RSSI Fingerprints: An Overview.” IEEE Access, vol. 9, 2021, pp. 127150–127174.
XV. Suroso, Dwi, Farid Adiyatma, Panarat Cherntanomwong, and Pitikhate Sooraksa. “Fingerprint Database Enhancement by Applying Interpolation and Regression Techniques for IoT-Based Indoor Localization.” Emerging Science Journal, vol. 4, 2022, pp. 167–189. 10.28991/esj-2021-SP1-012.
XVI. Yang, Junhua. “Indoor Localization System Using Dual-Frequency Bands and Interpolation Algorithm.” IEEE Internet of Things Journal, vol. 7, no. 11, 2020, pp. 11183–11194. 10.1109/JIOT.2020.2996610.
XVII. Zhang, Xing, et al. “WiFi-Based Indoor Localization with Interval Random Analysis and Improved Particle Swarm Optimization.” IEEE Transactions on Mobile Computing, vol. 23, no. 10, 2024, pp. 9120–9134.