Skip to main content
SpringerLink
Log in

Improvement of source localization via cellular network using machine learning approach

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

Abstract

Hyperbolic source localization and Taylor-series estimations are widely used and standardized by applying the time difference of a received signal between sensors. However, due to the different installation positions of sensors, data must be transmitted via a wide area and local network to arrive at a processing center. These are essential parameters in finding the correct position of a wave source. Therefore, machine learning-based prediction methods have drawn significant attention due to their outstanding execution and robust modeling potential. This paper aims to improve the source localization process, including the transmission rate occurring in a public network, through a combination of graphical modes, a Taylor-series expansion, and machine learning regression by developing so-called machine learning-based cross-correlation (ML-CCR) algorithms. The actual FM radio signals from three broadcast stations were used to train the model, and the regression algorithm was performed with another dataset containing untrained data. The experimental results indicated that the ML-CCR algorithm with random forest and boost regression provided more beneficial outcomes for range difference determinations and Taylor-series estimations than the standard cross-correlation technique. Moreover, our developed ML-CCR algorithm can improve the source localization error due to unwanted delay in a wide area and local network.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Availability of data and material

Not applicable

Code availability

Not applicable

References

  1. Zekavat, S. A., & Buehrer, R. M. (2012). Handbook of position location: Theory, practice, and advances. John Wiley & Sons Inc.

    Google Scholar 

  2. Sun, Y., Ho, K. C., & Wan, Q. (2019). Solution and analysis of TDOA localization of a near or distant source in closed form. IEEE Transactions on Signal Processing, 67(2), 320–335. https://doi.org/10.1109/TSP.2018.2879622.

    Article  Google Scholar 

  3. Li, X., & Wang, Y. (2021). Research on a factor graph-based robust UWB positioning algorithm in NLOS environments. Telecommunication Systems, 76, 207–217. https://doi.org/10.1007/s11235-020-00709-2

    Article  Google Scholar 

  4. Ben Halima, N., & Boujemâa, H. (2019). 3D WLS hybrid and non hybrid localization using TOA, TDOA, azimuth and elevation. Telecommunication Systems, 70, 97–104. https://doi.org/10.1007/s11235-018-0468-7

    Article  Google Scholar 

  5. Knapp, C., & Carter, G. (1976). The generalized correlation method for estimation of time delay. IEEE Transactions on Acoustics, Speech, and Signal Processing, 24(4), 320–327. https://doi.org/10.1109/TASSP.1976.1162830

    Article  Google Scholar 

  6. Jacovitti, G., & Scarano, G. (1993). Discrete time techniques for time delay estimation. IEEE Transactions on Signal Processing, 41(2), 525–533. https://doi.org/10.1109/78.193195

    Article  Google Scholar 

  7. Torrieri, D. .J. (1984). Statistical theory of passive location systems. IEEE Transactions on Aerospace and Electronic Systems, 20(2), 183–198. https://doi.org/10.1109/TAES.1984.310439

    Article  Google Scholar 

  8. Chan, Y. T., & Ho, K. C. (1994). A simple and efficient estimator for hyperbolic location. IEEE Transactions on Signal Processing, 42(8), 1905–1915. https://doi.org/10.1109/78.301830

    Article  Google Scholar 

  9. Foy, W. .H. (1976). Position-location solutions by Taylor-series estimation. IEEE Transactions on Aerospace and Electronic Systems, 12(2), 187–194. https://doi.org/10.1109/TAES.1976.308294

    Article  Google Scholar 

  10. Ho, K. C., & Yang, L. (2008). On the use of a calibration emitter for source localization in the presence of sensor position uncertainty. IEEE Transactions on Signal Processing, 56(12), 5758–5772. https://doi.org/10.1109/TSP.2008.929870

    Article  Google Scholar 

  11. Yang, L., & Ho, K. C. (2010). Alleviating sensor position error in source localization using calibration emitters at inaccurate locations. IEEE Transactions on Signal Processing, 58(1), 67–83. https://doi.org/10.1109/TSP.2009.2028947

    Article  Google Scholar 

  12. Zou, Y., Liu, H., Xie, W., & Wan, Q. (2017). Semidefinite programming methods for alleviating sensor position error in TDOA localization. IEEE Access, 5, 23111–23120. https://doi.org/10.1109/ACCESS.2017.2752206

    Article  Google Scholar 

  13. Zou, Y., & Liu, H. (2020). Semidefinite programming methods for alleviating clock synchronization bias and sensor position errors in TDOA localization. IEEE Signal Processing Letters, 27, 241–245. https://doi.org/10.1109/LSP.2020.2965822

    Article  Google Scholar 

  14. Ma, Z., & Ho, K. C. (2014). A study on the effects of sensor position error and the placement of calibration emitter for source localization. IEEE Transactions on Wireless Communications, 13(10), 5440–5452. https://doi.org/10.1109/TWC.2014.2341609

    Article  Google Scholar 

  15. Wang, Y., & Ho, K. C. (2013). TDOA source localization in the presence of synchronization clock bias and sensor position errors. IEEE Transactions on Signal Processing, 61(18), 4532–4544. https://doi.org/10.1109/TSP.2013.2271750

    Article  Google Scholar 

  16. Xiao, X., Zhao, S., Zhong, X., Jones, D. L., Cheng, E. S., & Li, H. (2015). A learning-based approach to direction of arrival estimation in noisy and reverberant environments. In The international conference on acoustics, speech, & signal processing (pp 2814–2818). https://doi.org/10.1109/ICASSP.2015.7178484.

  17. Bibb, D. A., Yun, Z., & Iskander, M. F. (2016). Machine learning for source localization in urban environments. In IEEE military communications conference (pp. 401–405). https://doi.org/10.1109/MILCOM.2016.7795360.

  18. Li, J., Lu, I. T., Lu, J.S. & Zhang, L. (2017). Robust kernel-based machine learning localization using NLOS TOAs or TDOAs. In IEEE Long Island systems, applications and technology conference (pp. 1–6). https://doi.org/10.1109/LISAT.2017.8001981

  19. Wu, C., Hou, H., Wang, W., Huang, Q., & Gao, X. (2018). TDOA based indoor positioning with NLOS identification by machine learning. In International conference on wireless communications and signal processing (pp. 1–6). https://doi.org/10.1109/WCSP.2018.8555654

  20. Zandian, R., & Witkowski, U. (2018). Differential NLOS error detection in UWB-based localization systems using logistic regression. In Workshop positioning, navigation communication (pp. 1–6). https://doi.org/10.1109/WPNC.2018.8555750.

  21. de Sousa, M. N., & Thoma, R. S. (2019). Applying random forest and multipath fingerprints to enhance TDOA localization systems. IEEE Antennas and Wireless Propagation Letters, 18(11), 2316–2320. https://doi.org/10.1109/LAWP.2019.2934466

    Article  Google Scholar 

  22. de Sousa, M. N. & Thoma, R. S. (2019). Enhanced localization systems with multipath fingerprints and machine learning. In IEEE international symposium on personal, indoor and mobile radio communications (pp. 1–6). https://doi.org/10.1109/PIMRC.2019.8904120.

  23. Cho, J., Hwang, D. & Kim, K. (2019). Improving TDoA based positioning accuracy using machine learning in a LoRaWan environment. In International conference on information networking (pp. 469–472).

  24. Phruksahiran, N., & Michanan, J. (2021). Iteration improvement of Taylor-series estimation using hyperbolic systems for FM-radio source localization in Bangkok. Signal, Image and Video Processing, 15, 247–254. https://doi.org/10.1007/s11760-020-01747-8

    Article  Google Scholar 

  25. Pedregosa, F., et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.

    Google Scholar 

  26. Ludloff, A. (2002). Praxixwissen radar und radarsignalverarbeitung (3rd ed.). Vieweg.

  27. Papula, L. (1998). Mathematik fur ingenieure und naturwissenschaftler Band 1 (8th ed.). Viewegs.

  28. Zeidler, E. (1996). Teubner-Tashenbuch der mathematik (8th ed.). Teubner.

Download references

Funding

Not applicable

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Chulachomklao Royal Military Academy, Nakhon Nayok, 26001, Thailand

    Narathep Phruksahiran

Authors
  1. Narathep Phruksahiran
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Not applicable

Corresponding author

Correspondence to Narathep Phruksahiran.

Ethics declarations

Conflict of interest

Not applicable

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Phruksahiran, N. Improvement of source localization via cellular network using machine learning approach. Telecommun Syst 82, 291–299 (2023). https://doi.org/10.1007/s11235-022-00986-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-022-00986-z

Keywords

Search

Navigation