Abstract
We introduce a parameter optimization strategy to enhance the accuracy of an indoor positioning system. The indoor positioning system of interest is composed of subsequent stages of processing of reference reduced signal strength indicator (RSSI) streams, Gaussian processes-based estimation of probabilistic radio-frequency maps, and an adaptive particle filter that is used to infer the trajectory of the tracked object. Each stage has its own model parameters, which can be evaluated by the accuracy of the final trajectory estimations given their ground-truth counterparts. We make use of an open dataset that includes RSSI data on reference points, RSSI data related to trajectories and their corresponding ground-truth positions. By being able to evaluate the estimations, we develop a Monte Carlo particle swarm optimization strategy to search for the best parameter configuration that minimizes the trajectory error. The time performance of the optimization strategy is also improved by artificially discretizing the parameters space, so that the stages can use the previously processed streams or radio maps. We show that the strategy can both improve accuracy and decrease the search time with respect to a grid-based search strategy.
Keywords
Fingerprinting Indoor Positioning Monte Carlo Swarm Optimization Radio Frequency Map Estimation Reduced Signal Strength Indicator
References
- F. Zafari, A. Gkelias, and K. K. Leung, “A survey of indoor localization systems and technologies,” IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2568–2599, 2019.
- R. Brena, J. Garcia-Vázquez, C. Galván Tejada, D. Muñoz, C. Vargas-Rosales, J. Fangmeyer Jr, and A. Palma, “Evolution of indoor positioning technologies: A survey,” Journal of Sensors, vol. 2017, 03 2017.
- T. Arsan, “Accurate indoor positioning with ultra-wide band sensors,” Turkish Journal of Electrical Engineering and Computer Sciences, vol. 28, no. 2, pp. 1014–1029, 2020.
- M. Liu, L. Cheng, K. Qian, J. Wang, J. Wang, and Y. Liu, “Indoor acoustic localization: a survey,” Hum.-Centric Comput. Inf. Sci., vol. 10, Jan. 2020.
- D. Plikynas, A. Žvironas, A. Budrionis, and M. Gudauskis, “Indoor navigation systems for visually impaired persons: Mapping the features of existing technologies to user needs,” Sensors, vol. 20, no. 3, 2020.
- A. De-La-Llana-Calvo, J.-L. Lázaro-Galilea, A. Gardel-Vicente, D. Rodriguez-Navarro, I. Bravo-Muñoz, and F. Espinosa-Zapata, “Characterization of multipath effects in indoor positioning systems by aoa and poa based on optical signals,” Sensors, vol. 19, no. 4, 2019.
- A. Pérez-Navarro, J. Torres-Sospedra, R. Montoliu, J. Conesa, R. Berkvens, G. Caso, C. Costa, N. Dorigatti, N. Hernández, S. Knauth, E. S. Lohan, J. Machaj, A. Moreira, and P. Wilk, “Challenges of fingerprinting in indoor positioning and navigation,” in Geographical and Fingerprinting Data to Create Systems for Indoor Positioning and Indoor/Outdoor Navigation (J. Conesa, A. Pérez-Navarro, J. Torres-Sospedra, and R. Montoliu, eds.), Intelligent Data-Centric Systems, pp. 1–20, Academic Press, 2019.
- S. Subedi and J.-Y. Pyun, “Practical fingerprinting localization for indoor positioning system by using beacons,” Journal of Sensors, vol. 2017, no. 1, p. 9742170, 2017.
- F. S. Daniş and A. T. Cemgil, “Model-based localization and tracking using bluetooth low-energy beacons,” Sensors, vol. 17, no. 11, 2017.
- R. Shrestha, D. Romero, and S. P. Chepuri, “Spectrum surveying: Active radio map estimation with autonomous uavs,” IEEE Transactions on Wireless Communications, vol. 22, no. 1, pp. 627–641, 2023.
- M. Nabati and S. A. Ghorashi, “A real-time fingerprint-based indoor positioning using deep learning and preceding states,” Expert Systems with Applications, vol. 213, p. 118889, 2023.
- F. Alhomayani and M. H. Mahoor, “Deep learning methods for fingerprint-based indoor positioning: a review,” Journal of Location Based Services, vol. 14, no. 3, pp. 129–200, 2020.
- Y. Zhao, C. Liu, L. S. Mihaylova, and F. Gunnarsson, “Gaussian processes for rss fingerprints construction in indoor localization,” in 2018 21st International Conference on Information Fusion (FUSION), pp. 1377–1384, 2018.
- R. Guan, A. Zhang, M. Li, and Y. Wang, “Measuring uncertainty in signal fingerprinting with gaussian processes going deep,” in 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–8, 2021.
- F. S. Daniş, “Live RSSI filtering for indoor positioning with bluetooth low-energy,” in 2022 IEEE 12th International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–8, 2022.
- F. S. Daniş, A. T. Cemgil, and C. Ersoy, “Adaptive sequential monte carlo filter for indoor positioning and tracking with bluetooth low energy beacons,” IEEE Access, vol. 9, pp. 37022–37038, 2021.
- F. S. Daniş, A. T. Naskali, A. T. Cemgil, and C. Ersoy, “An indoor localization dataset and data collection framework with high precision position annotation,” Pervasive and Mobile Computing, vol. 81, p. 101554, 2022.
- D. Luengo, L. Martino, M. Bugallo, V. Elvira, and S. Särkkä, “A survey of monte carlo methods for parameter estimation,” EURASIP Journal on Advances in Signal Processing, vol. 2020, may 2020.
- O. Akyildiz, D. Crisan, and J. Miguez, “Parallel sequential monte carlo for stochastic gradient-free nonconvex optimization,” Statistics and Computing, vol. 30, 11 2020.
- S. Roberts, M. Osborne, M. Ebden, S. Reece, N. Gibson, and S. Aigrain, “Gaussian processes for time-series modelling,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 371, no. 1984, p. 20110550, 2013.
- C. E. Rasmussen, Gaussian Processes in Machine Learning, pp. 63–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004.
- D. Barber, Bayesian Reasoning and Machine Learning. USA: Cambridge University Press, 2012.
- F. S. Daniş, “Practical and parameterized fingerprinting through maximal filtering for indoor positioning,” IEEE Journal of Indoor and Seamless Positioning and Navigation, vol. 1, pp. 199–210, 2023.
Abstract
We introduce a parameter optimization strategy to enhance the accuracy of an indoor positioning system. The indoor positioning system of interest is composed of subsequent stages of processing of reference reduced signal strength indicator (RSSI) streams, Gaussian processes-based estimation of probabilistic radio-frequency maps, and an adaptive particle filter that is used to infer the trajectory of the tracked object. Each stage has its own model parameters, which can be evaluated by the accuracy of the final trajectory estimations given their ground-truth counterparts. We make use of an open dataset that includes RSSI data on reference points, RSSI data related to trajectories and their corresponding ground-truth positions. By being able to evaluate the estimations, we develop a Monte Carlo particle swarm optimization strategy to search for the best parameter configuration that minimizes the trajectory error. The time performance of the optimization strategy is also improved by artificially discretizing the parameters space, so that the stages can use the previously processed streams or radio maps. We show that the strategy can both improve accuracy and decrease the search time with respect to a grid-based search strategy.
Keywords
Fingerprinting Indoor Positioning Monte Carlo Swarm Optimization Radio Frequency Map Estimation Reduced Signal Strength Indicator
References
- F. Zafari, A. Gkelias, and K. K. Leung, “A survey of indoor localization systems and technologies,” IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2568–2599, 2019.
- R. Brena, J. Garcia-Vázquez, C. Galván Tejada, D. Muñoz, C. Vargas-Rosales, J. Fangmeyer Jr, and A. Palma, “Evolution of indoor positioning technologies: A survey,” Journal of Sensors, vol. 2017, 03 2017.
- T. Arsan, “Accurate indoor positioning with ultra-wide band sensors,” Turkish Journal of Electrical Engineering and Computer Sciences, vol. 28, no. 2, pp. 1014–1029, 2020.
- M. Liu, L. Cheng, K. Qian, J. Wang, J. Wang, and Y. Liu, “Indoor acoustic localization: a survey,” Hum.-Centric Comput. Inf. Sci., vol. 10, Jan. 2020.
- D. Plikynas, A. Žvironas, A. Budrionis, and M. Gudauskis, “Indoor navigation systems for visually impaired persons: Mapping the features of existing technologies to user needs,” Sensors, vol. 20, no. 3, 2020.
- A. De-La-Llana-Calvo, J.-L. Lázaro-Galilea, A. Gardel-Vicente, D. Rodriguez-Navarro, I. Bravo-Muñoz, and F. Espinosa-Zapata, “Characterization of multipath effects in indoor positioning systems by aoa and poa based on optical signals,” Sensors, vol. 19, no. 4, 2019.
- A. Pérez-Navarro, J. Torres-Sospedra, R. Montoliu, J. Conesa, R. Berkvens, G. Caso, C. Costa, N. Dorigatti, N. Hernández, S. Knauth, E. S. Lohan, J. Machaj, A. Moreira, and P. Wilk, “Challenges of fingerprinting in indoor positioning and navigation,” in Geographical and Fingerprinting Data to Create Systems for Indoor Positioning and Indoor/Outdoor Navigation (J. Conesa, A. Pérez-Navarro, J. Torres-Sospedra, and R. Montoliu, eds.), Intelligent Data-Centric Systems, pp. 1–20, Academic Press, 2019.
- S. Subedi and J.-Y. Pyun, “Practical fingerprinting localization for indoor positioning system by using beacons,” Journal of Sensors, vol. 2017, no. 1, p. 9742170, 2017.
- F. S. Daniş and A. T. Cemgil, “Model-based localization and tracking using bluetooth low-energy beacons,” Sensors, vol. 17, no. 11, 2017.
- R. Shrestha, D. Romero, and S. P. Chepuri, “Spectrum surveying: Active radio map estimation with autonomous uavs,” IEEE Transactions on Wireless Communications, vol. 22, no. 1, pp. 627–641, 2023.
- M. Nabati and S. A. Ghorashi, “A real-time fingerprint-based indoor positioning using deep learning and preceding states,” Expert Systems with Applications, vol. 213, p. 118889, 2023.
- F. Alhomayani and M. H. Mahoor, “Deep learning methods for fingerprint-based indoor positioning: a review,” Journal of Location Based Services, vol. 14, no. 3, pp. 129–200, 2020.
- Y. Zhao, C. Liu, L. S. Mihaylova, and F. Gunnarsson, “Gaussian processes for rss fingerprints construction in indoor localization,” in 2018 21st International Conference on Information Fusion (FUSION), pp. 1377–1384, 2018.
- R. Guan, A. Zhang, M. Li, and Y. Wang, “Measuring uncertainty in signal fingerprinting with gaussian processes going deep,” in 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–8, 2021.
- F. S. Daniş, “Live RSSI filtering for indoor positioning with bluetooth low-energy,” in 2022 IEEE 12th International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–8, 2022.
- F. S. Daniş, A. T. Cemgil, and C. Ersoy, “Adaptive sequential monte carlo filter for indoor positioning and tracking with bluetooth low energy beacons,” IEEE Access, vol. 9, pp. 37022–37038, 2021.
- F. S. Daniş, A. T. Naskali, A. T. Cemgil, and C. Ersoy, “An indoor localization dataset and data collection framework with high precision position annotation,” Pervasive and Mobile Computing, vol. 81, p. 101554, 2022.
- D. Luengo, L. Martino, M. Bugallo, V. Elvira, and S. Särkkä, “A survey of monte carlo methods for parameter estimation,” EURASIP Journal on Advances in Signal Processing, vol. 2020, may 2020.
- O. Akyildiz, D. Crisan, and J. Miguez, “Parallel sequential monte carlo for stochastic gradient-free nonconvex optimization,” Statistics and Computing, vol. 30, 11 2020.
- S. Roberts, M. Osborne, M. Ebden, S. Reece, N. Gibson, and S. Aigrain, “Gaussian processes for time-series modelling,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 371, no. 1984, p. 20110550, 2013.
- C. E. Rasmussen, Gaussian Processes in Machine Learning, pp. 63–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004.
- D. Barber, Bayesian Reasoning and Machine Learning. USA: Cambridge University Press, 2012.
- F. S. Daniş, “Practical and parameterized fingerprinting through maximal filtering for indoor positioning,” IEEE Journal of Indoor and Seamless Positioning and Navigation, vol. 1, pp. 199–210, 2023.
Details
| Primary Language | English |
|---|---|
| Subjects | Control Engineering, Mechatronics and Robotics (Other) |
| Journal Section | Research Article |
| Authors | |
| Early Pub Date | September 24, 2025 |
| Publication Date | September 30, 2025 |
| Submission Date | February 21, 2025 |
| Acceptance Date | July 22, 2025 |
| Published in Issue | Year 2025 Volume: 8 Issue: 3 |
Cite
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