Improved LTE Fingerprint Positioning Through Clustering-based Repeater Detection and Outlier Removal
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초록
In weighted k-nearest neighbor (WkNN)-based Fingerprinting positioning step, a process of comparing the requested positioning signal with signal information for each reference point stored in the fingerprint DB is performed. At this time, the higher the number of matched base station identifiers, the higher the possibility that the terminal exists in the corresponding location, and in fact, an additional weight is added to the location in proportion to the number of matching base stations. On the other hand, if the matching number of base stations is small, the selected candidate reference point has high dependence on the similarity value of the signal. But one problem arises here. The positioning signal can be compared with the repeater signal in the signal information stored on the DB, and the corresponding reference point can be selected as a candidate location. The selected reference point is likely to be an outlier, and if a certain weight is applied to the corresponding location, the error of the estimated location information increases. In order to solve this problem, this paper proposes a WkNN technique including an outlier removal function. To this end, it is first determined whether the repeater signal is included in the DB information of the matched base station. If the reference point for the repeater signal is selected as the candidate position, the reference position corresponding to the outlier is removed based on the clustering technique. The performance of the proposed technique is verified through data acquired in Seocho 1 and 2 dongs in Seoul.
Abstract
In weighted k-nearest neighbor (WkNN)-based Fingerprinting positioning step, a process of comparing the requested positioning signal with signal information for each reference point stored in the fingerprint DB is performed. At this time, the higher the number of matched base station identifiers, the higher the possibility that the terminal exists in the corresponding location, and in fact, an additional weight is added to the location in proportion to the number of matching base stations. On the other hand, if the matching number of base stations is small, the selected candidate reference point has high dependence on the similarity value of the signal. But one problem arises here. The positioning signal can be compared with the repeater signal in the signal information stored on the DB, and the corresponding reference point can be selected as a candidate location. The selected reference point is likely to be an outlier, and if a certain weight is applied to the corresponding location, the error of the estimated location information increases. In order to solve this problem, this paper proposes a WkNN technique including an outlier removal function. To this end, it is first determined whether the repeater signal is included in the DB information of the matched base station. If the reference point for the repeater signal is selected as the candidate position, the reference position corresponding to the outlier is removed based on the clustering technique. The performance of the proposed technique is verified through data acquired in Seocho 1 and 2 dongs in Seoul.
참고문헌 (11)
- Alazab, M., "Trust, Security and Privacy for Big Data" (2022), CRC Press
- 조성윤, "LTE Signal Propagation Model-based Fingerprint DB Generation for Positioning in Emergency Rescue Situation", Journal of Positioning, Navigation, and Timing, 9(3), pp.157~167 (2020), 사단법인 항법시스템학회[DOI]
- Cho, S. Y., "Simultaneous LTE Signal Propagation Modelling and Base Station Positioning Based on Multiple Virtual Locations", Sensors, 22, pp.5917~ (2022)[DOI]
- Huang, H. S., "Location Based Services: Ongoing Evolution and Research Agenda", Journal of Location Based Services, 12, pp.63~93 (2018)[DOI]
- Ismail, A. H., "WiFi RSS Fingerprint Database Construction for Mobile Robot Indoor Positioning System", 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp.1561~1566 (2016)
- Khoo, H. W., "Enhanced Radio Map Interpolation Methods Based on Dimensionality Reduction and Clustering", Electronics, 11, pp.2581~ (2022)[DOI]
- Jae Uk Kwon, "CNN-based Adaptive K for Improving Positioning Accuracy in W-kNN-based LTE Fingerprint Positioning", Journal of Positioning, Navigation, and Timing, 11(3), pp.217~227 (2022), 사단법인 항법시스템학회[DOI]
- Liu, S., "Performance analysis of adaptive K for weighted K-nearest neighbor based indoor positioning", The 2022 IEEE 95th Vehicular Technology Conference: VTC2022-Spring will be held in Helsinki (2022)
- Xia, S., "Indoor Fingerprint Positioning Based on Wi-Fi: An Overview", ISPRS International Journal of Geo-Information, 6, pp.135~ (2017)[DOI]
- Xu, S., "Adaptive Residual Weighted K-Nearest Neighbor Fingerprint Positioning Algorithm Based on Visible Light Communication", Sensors, 20, pp.4432~ (2020)[DOI]
- Yuan, C., "Research on K-Value Selection Method of K-Means Clustering Algorithm", Multidisciplinary Scientific Journal, 2, pp.226~235 (2019)[DOI]