A Research on Positioning Algorithm Based on RPCA in Sparse Fingerprint Environment
DOI:
https://doi.org/10.34010/injiiscom.v6i2.14980Keywords:
RSS, RPCA, Fingerprint Matching, Indoor PositioningAbstract
The indoor positioning method based on Wi-Fi fingerprinting has the advantages of simple acquisition and low cost. However, during signal collection, the presence of significant noise in the environment can cause fluctuations in signal strength measurements due to environmental variations. Additionally, a large number of fingerprints usually need to be collected to achieve high positioning accuracy. To address these issues, this paper proposes a positioning method based on a robust principal component analysis algorithm (RPCA) in a sparse fingerprint environment. Firstly, considering the outlier noise present in the collected signals, purification is performed based on signal measurement weights, and the refined fingerprints are stored in the fingerprint database. Secondly, given the high cost of collecting fingerprints, this paper generates some virtual fingerprints near reference points based on a transmission loss model, all of which are stored in an offline fingerprint database. Finally, adaptive K-value fingerprint matching is used to obtain the final results. The results show that the proposed algorithm can improve positioning accuracy in a sparse fingerprint environment.
References
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Tinh, P. D., Bui, H. H., & Nguyen, D. C. (2023). A genetic based indoor positioning algorithm using Wi-Fi received signal strength and motion data. IAES International Journal of Artificial Intelligence, 12(1), 328.
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Wang, X., Zhu, C., Tian, Y., Wang, X., Lin, Y., & Li, W. (2023). Optimization and performance analysis of Wi-Fi indoor fingerprint localization algorithm. 2023 5th International Conference on Frontiers Technology of Information and Computer (ICFTIC),
Wei, Y., & Zheng, R. (2021). Efficient Wi-Fi fingerprint crowdsourcing for indoor localization. IEEE Sensors Journal, 22(6), 5055-5062.
Xu, J., Li, Z., Zhang, K., Yang, J., Gao, N., Zhang, Z., & Meng, Z. (2023). The principle, methods and recent progress in RFID positioning techniques: A review. IEEE Journal of Radio Frequency Identification, 7, 50-63.
Yen, L., Yan, C.-H., Renu, S., Belay, A., Lin, H.-P., & Ye, Y.-S. (2017). A modified WKNN indoor Wi-Fi localization method with differential coordinates. 2017 International Conference on Applied System Innovation (ICASI),
Zekavat, S., Buehrer, R. M., Durgin, G. D., Lovisolo, L., Wang, Z., Goh, S. T., & Ghasemi, A. (2021). An overview on position location: Past, present, future. International Journal of Wireless Information Networks, 28, 45-76.
Zhou, R., Yang, Y., & Chen, P. (2021). An RSS transform—Based WKNN for indoor positioning. Sensors, 21(17), 5685.
Zhuang, Y., Zhang, C., Huai, J., Li, Y., Chen, L., & Chen, R. (2022). Bluetooth localization technology: Principles, applications, and future trends. IEEE Internet of Things Journal, 9(23), 23506-23524.
Abraha, A. T., & Wang, B. (2024). A Survey on Scalable Wireless Indoor Localization: Techniques, Approaches and Directions. Wireless Personal Communications, 136(3), 1455-1496.
Asaad, S. M., & Maghdid, H. S. (2022). A comprehensive review of indoor/outdoor localization solutions in IoT era: Research challenges and future perspectives. Computer Networks, 212, 109041.
Bazin, S., & Navaie, K. (2023). Improving Indoor Positioning Accuracy Using RIS-based RSS Optimization. 2023 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit),
Bonthu, B., & Mohan, S. (2023). Combining Wi-Fi Fingerprinting and Pedestrian Dead Reckoning to Mitigate External Factors for a Sustainable Indoor Positioning System. Sustainability, 15(14), 10943.
Duong, T. H., Trinh, A. V., & Hoang, M. K. (2024). Efficient and Accurate Indoor Positioning System: A Hybrid Approach Integrating PCA, WKNN, and Linear Regression. Journal of Communications, 19(1).
Ezhumalai, B., Song, M., & Park, K. (2021). An efficient indoor positioning method based on Wi-Fi RSS fingerprint and classification algorithm. Sensors, 21(10), 3418.
Gismalla, M. S. M., Azmi, A. I., Salim, M. R. B., Abdullah, M. F. L., Iqbal, F., Mabrouk, W. A., Othman, M. B., Ashyap, A. Y., & Supa’at, A. S. M. (2022). Survey on device to device (D2D) communication for 5GB/6G networks: Concept, applications, challenges, and future directions. IEEE Access, 10, 30792-30821.
Hou, C., Xie, Y., & Zhang, Z. (2022). FCLoc: A novel indoor Wi-Fi fingerprints localization approach to enhance robustness and positioning accuracy. IEEE Sensors Journal, 23(7), 7153-7167.
Ilias, B., Shukor, S. A. A., Adom, A. H., Abd Rahim, N., Ibrahim, M. F., & Yaacob, S. (2016). Indoor mobile robot localization using KNN. 2016 6th IEEE International Conference on Control System, Computing and Engineering (ICCSCE),
Isaia, C., & Michaelides, M. P. (2023). A review of wireless positioning techniques and technologies: From smart sensors to 6G. Signals, 4(1), 90-136.
Ji, Y., Zhao, X., Wei, Y., & Wang, C. (2022). Generating indoor Wi-Fi fingerprint map based on crowdsourcing. Wireless Networks, 28(3), 1053-1065.
Lan, T., Wang, X., Chen, Z., Zhu, J., & Zhang, S. (2022). Fingerprint augment based on super-resolution for WiFi fingerprint based indoor localization. IEEE Sensors Journal, 22(12), 12152-12162.
Lee, S.-H., Kim, W.-Y., & Seo, D.-H. (2022). Automatic self-reconstruction model for radio map in Wi-Fi fingerprinting. Expert Systems with Applications, 192, 116455.
Leitch, S. G., Ahmed, Q. Z., Abbas, W. B., Hafeez, M., Laziridis, P. I., Sureephong, P., & Alade, T. (2023). On indoor localization using WiFi, BLE, UWB, and IMU technologies. Sensors, 23(20), 8598.
Li, H., Qian, Z., Tian, C., & Wang, X. (2020). TILoc: Improving the robustness and accuracy for fingerprint-based indoor localization. IEEE Internet of Things Journal, 7(4), 3053-3066.
Li, X., Pang, H., Li, G., Jiang, J., Zhang, H., Gu, C., & Yuan, D. (2024). Wireless Positioning: Technologies, Applications, Challenges, and Future Development Trends. CMES-Computer Modeling in Engineering & Sciences, 139(2), 1135-1166.
Lin, H., Purmehdi, H., Fei, X., Zhao, Y., Isac, A., Louafi, H., & Peng, W. (2023). Two-stage clustering for improve indoor positioning accuracy. Automation in Construction, 154, 104981.
Liu, H., Yang, J., Sidhom, S., Wang, Y., Chen, Y., & Ye, F. (2013). Accurate WiFi based localization for smartphones using peer assistance. IEEE Transactions on Mobile Computing, 13(10), 2199-2214.
Liu, W., Zhang, Y., Deng, Z., & Zhou, H. (2023). Low-cost indoor wireless fingerprint location database construction methods: A review. IEEE Access, 11, 37535-37545.
Ma, W., Fang, X., Liang, L., & Du, J. (2024). Research on indoor positioning system algorithm based on UWB technology. Measurement: Sensors, 33, 101121.
Moradbeikie, A., Keshavarz, A., Rostami, H., Paiva, S., & Lopes, S. I. (2021). GNSS-free outdoor localization techniques for resource-constrained IoT architectures: A literature review. Applied Sciences, 11(22), 10793.
NSC, J., Wahab, N., Sunar, N., Ariffin, S., Wong, K., & Aun, Y. (2022). Indoor Positioning System: A Review. International Journal of Advanced Computer Science and Applications, 13(6).
Shu, M., Chen, G., Zhang, Z., & Xu, L. (2022). Indoor geomagnetic positioning using direction-aware multiscale recurrent neural networks. IEEE Sensors Journal, 23(3), 3321-3333.
Song, J., Li, W., Duan, C., Wang, L., Fan, Y., & Zhu, X. (2024). An Optimization-Based Indoor-Outdoor Seamless Positioning Method Integrating GNSS RTK, PS, and VIO. IEEE Transactions on Circuits and Systems II: Express Briefs.
Sun, K., Sun, L., & Chen, T. (2024). Indoor Positioning Model Based on Principal Component Extraction of Support Vector Regression. 2024 36th Chinese Control and Decision Conference (CCDC),
Tang, C., Sun, W., Zhang, X., Zheng, J., Wu, W., & Sun, J. (2023). A novel fingerprint positioning method applying vision-based definition for wifi-based localization. IEEE Sensors Journal, 23(14), 16092-16106.
Tinh, P. D., Bui, H. H., & Nguyen, D. C. (2023). A genetic based indoor positioning algorithm using Wi-Fi received signal strength and motion data. IAES International Journal of Artificial Intelligence, 12(1), 328.
Wang, B., Gan, X., Liu, X., Yu, B., Jia, R., Huang, L., & Jia, H. (2020). A novel weighted KNN algorithm based on RSS similarity and position distance for Wi-Fi fingerprint positioning. IEEE Access, 8, 30591-30602.
Wang, X., Zhu, C., Tian, Y., Wang, X., Lin, Y., & Li, W. (2023). Optimization and performance analysis of Wi-Fi indoor fingerprint localization algorithm. 2023 5th International Conference on Frontiers Technology of Information and Computer (ICFTIC),
Wei, Y., & Zheng, R. (2021). Efficient Wi-Fi fingerprint crowdsourcing for indoor localization. IEEE Sensors Journal, 22(6), 5055-5062.
Xu, J., Li, Z., Zhang, K., Yang, J., Gao, N., Zhang, Z., & Meng, Z. (2023). The principle, methods and recent progress in RFID positioning techniques: A review. IEEE Journal of Radio Frequency Identification, 7, 50-63.
Yen, L., Yan, C.-H., Renu, S., Belay, A., Lin, H.-P., & Ye, Y.-S. (2017). A modified WKNN indoor Wi-Fi localization method with differential coordinates. 2017 International Conference on Applied System Innovation (ICASI),
Zekavat, S., Buehrer, R. M., Durgin, G. D., Lovisolo, L., Wang, Z., Goh, S. T., & Ghasemi, A. (2021). An overview on position location: Past, present, future. International Journal of Wireless Information Networks, 28, 45-76.
Zhou, R., Yang, Y., & Chen, P. (2021). An RSS transform—Based WKNN for indoor positioning. Sensors, 21(17), 5685.
Zhuang, Y., Zhang, C., Huai, J., Li, Y., Chen, L., & Chen, R. (2022). Bluetooth localization technology: Principles, applications, and future trends. IEEE Internet of Things Journal, 9(23), 23506-23524.
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Published
2025-02-01
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How to Cite
[1]
“A Research on Positioning Algorithm Based on RPCA in Sparse Fingerprint Environment”, Int. J. Inform. Inf. Sys. and Comp. Eng., vol. 6, no. 2, pp. 225–245, Feb. 2025, doi: 10.34010/injiiscom.v6i2.14980.
