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Research on RFID Indoor Localization Algorithm Based on Virtual Tags and Fusion of LANDMARC and Kalman Filter

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6GN for Future Wireless Networks (6GN 2023)

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Abstract

With the increase in demand for indoor positioning accuracy, the traditional LANDMARC algorithm introduces numerous tags leading to interferences between them. To address this issue and reduce costs, a new RFID indoor positioning algorithm was proposed. This novel approach was based on the integration of the Kalman filter and LANDMARC algorithm, along with the introduction of virtual tags. The primary aim of this algorithm was to reduce deployment cost and positioning errors while achieving more precise tag motion and position change descriptions. Moreover, this algorithm utilized the signal strength model of LANDMARC and the estimation results of the Kalman filter to infer and correct the target position with nuance. Simulation experiments show that the algorithm produces reliable results with high positioning accuracy, robustness, and adaptability.

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References

  1. Zafari, F., Gkelias, A., Leung, K.K.: A survey of indoor localization systems and technologies. IEEE Commun. Surv. Tutor. 21(3), 2568–2599 (2019)

    Article  Google Scholar 

  2. Dong, F., Shen, C., Zhang, J., et al.: A TOF and Kalman filtering joint algorithm for IEEE802. 15.4 a UWB Locating. In: 2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference, pp. 948–951. IEEE (2016)

    Google Scholar 

  3. Wei, H., Wang, D.: Research on improved RFID indoor location algorithm based on LANDMARC. In: Proceedings of the 2022 4th International Conference on Robotics, Intelligent Control and Artificial Intelligence, pp. 1294–1297 (2022)

    Google Scholar 

  4. Ren, J., Bao, K., Zhang, G.: LANDMARC indoor positioning algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network. Int. J. Distrib. Sens. Netw. SAGE Publications Sage UK: London, England 16(2), 1550147720907831 (2020)

    Google Scholar 

  5. Hu, B., Peng, H., Sun, Z.: LANDMARC localization algorithm based on weight optimization. Chin. J. Electron. 27(6), 1291–1296 (2018)

    Article  Google Scholar 

  6. Li, L., Zheng, J., Luo, W.: RFID indoor positioning algorithm based on proximal policy optimization. Comput. Sci. 2021(48), 274–281 (2021)

    Google Scholar 

  7. Fazzinga, B., Flesca, S., Furfaro, F., et al.: Interpreting RFID tracking data for simultaneously moving objects: an offline sampling-based approach. Expert Syst. Appl. 152, 113368 (2020)

    Article  Google Scholar 

  8. Li, X., Zhang, Y., Marsic, I., etal.: Deep learning for RFID-based activity recognition. In: Proceedings of the 14th ACM Conference on Embedded Network Sensor Systems CD-ROM, pp. 164–175 (2016)

    Google Scholar 

  9. Wu, X., Deng, F., Chen, Z.: RFID 3D-landmarc localization algorithm based on quantum particle swarm optimization. Electronics 7(2), 19 (2018). Tan, P., Tsinakwadi, T.H., Xu, Z., et al.: Sing-ant: RFID indoor positioning system using single antenna with multiple beams based on LANDMARC Algorithm. Appl. Sci. 12(13), 6751 (2022)

    Google Scholar 

  10. Liu, X., Wen, M., Qin, G., et al.: LANDMARC with improved k-nearest algorithm for RFID location system. In: 2016 2nd IEEE International Conference on Computer and Communications (ICCC), pp. 2569–2572. IEEE (2016)

    Google Scholar 

  11. Zhou, J., Shi, J.: A comprehensive multi-factor analysis on RFID localization capability. Adv. Eng. Inform. 25(1), 32–40 (2011)

    Article  Google Scholar 

  12. Li, H., Chan, G., Wong, J.K.W., et al.: Real-time locating systems applications in construction. Autom. Constr. 63, 37–47 (2016)

    Article  Google Scholar 

  13. Jiang, T., Huang, Y., Wang, Y.: Study on improved LANDMARC node localization algorithm. In: Xie, A., Huang, X. (eds.) Advances in Electrical Engineering and Automation. AISC, vol. 139, pp. 415–421. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-27951-5_62

  14. Li, J., Wu, Z., Wu, C., et al.: An inexact dual fast gradient-projection method for separable convex optimization with linear coupled constraints. J. Optim. Theory Appl. 168, 153–171 (2016)

    Article  MathSciNet  Google Scholar 

  15. Omer, M., Tian, G.Y.: Indoor distance estimation for passive UHF RFID tag based on RSSI and RCS. Measurement 127, 425–430 (2018)

    Article  Google Scholar 

  16. Chai, J., Wu, C., Zhao, C., et al.: Reference tag supported RFID tracking using robust support vector regression and Kalman filter. Adv. Eng. Inform. 32, 1–10 (2017)

    Article  Google Scholar 

  17. Wu, X., Deng, F., Chen, Z.: Rfid 3D-landmarc localization algorithm based on quantum particle swarm optimization. Electronics 7(2), 19 (2018)

    Article  Google Scholar 

  18. Xu, H., Wu, M., Li, P., et al.: An RFID indoor positioning algorithm based on support vector regression. Sensors 18(5), 1504 (2018)

    Article  Google Scholar 

  19. Zhu, J., Xu, H.: Review of RFID-based indoor positioning technology. In: Barolli, L., Xhafa, F., Javaid, N., Enokido, T. (eds.) IMIS 2018. AISC, vol. 773, pp. 632–641. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-93554-6_62

    Chapter  Google Scholar 

  20. Shirehjini, A.A.N., Shirmohammadi, S.: Improving accuracy and robustness in HF-RFID-based indoor positioning with Kalman filtering and Tukey smoothing. IEEE Trans. Instrum. Meas. 69(11), 9190–9202 (2020)

    Article  Google Scholar 

  21. Li, N., Ma, H., Yang, C.: Interval Kalman filter based RFID indoor positioning. In: 2016 Chinese Control and Decision Conference (CCDC), pp. 6958–6963. IEEE (2016)

    Google Scholar 

  22. Xu, H., Ding, Y., Li, P., et al.: An RFID indoor positioning algorithm based on Bayesian probability and K-nearest neighbor. Sensors 17(8), 1806 (2017)

    Article  Google Scholar 

  23. Zeng, Y., Chen, X., Li, R., et al.: UHF RFID indoor positioning system with phase interference model based on double tag array. IEEE Access 7, 76768–76778 (2019)

    Article  Google Scholar 

  24. Wang, C., Shi, Z., Wu, F., et al.: An RFID indoor positioning system by using particle swarm optimization-based artificial neural network. In: 2016 International Conference on Audio, Language and Image Processing (ICALIP), pp. 738–742. IEEE (2016)

    Google Scholar 

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Authors and Affiliations

  1. Shanghai Dianji University, Shanghai, China

    Wu Jiangbo, Liu Wenjun & Liu Hong

Authors
  1. Wu Jiangbo
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  2. Liu Wenjun
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  3. Liu Hong
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Corresponding author

Correspondence to Wu Jiangbo .

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Editors and Affiliations

  1. Shanghai Dianji University, Shanghai, China

    Jingchao Li

  2. Kanagawa University, Yokohama, Japan

    Bin Zhang

  3. Shanghai University of Electric Power, Yangpu, China

    Yulong Ying

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© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Jiangbo, W., Wenjun, L., Hong, L. (2024). Research on RFID Indoor Localization Algorithm Based on Virtual Tags and Fusion of LANDMARC and Kalman Filter. In: Li, J., Zhang, B., Ying, Y. (eds) 6GN for Future Wireless Networks. 6GN 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 554. Springer, Cham. https://doi.org/10.1007/978-3-031-53404-1_20

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  • DOI: https://doi.org/10.1007/978-3-031-53404-1_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53403-4

  • Online ISBN: 978-3-031-53404-1

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