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Machine-learning-assisted tracking of magnetic objects using quantum diamond magnetometry

Fernando Meneses1,2,*, Christopher T.-K. Lew1,3,*, Anand Sivamalai4, Andy Sayers4, Brant C. Gibson3, Andrew D. Greentree3, Lloyd C. L. Hollenberg1,2, and David A. Simpson1,5,†

  • 1School of Physics, University of Melbourne, Parkville, Victoria 3010, Australia
  • 2Centre for Quantum Computation and Communication Technology, School of Physics, University of Melbourne, Parkville, Victoria 3010, Australia
  • 3School of Science, RMIT University, Victoria, 3001, Australia
  • 4Phasor Innovation Pty Ltd, 155 Straws Lane, Hesket, Victoria 3442, Australia
  • 5ARC Centre of Excellence in Quantum Biotechnology, School of Physics, University of Melbourne, Parkville, Victoria 3010, Australia

  • *These authors contributed equally to this work.
  • †Contact author: simd@unimelb.edu.au
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Phys. Rev. Applied 26, 054003 – Published 2 November, 2026

DOI: https://doi.org/10.1103/vjtb-d8kb

Abstract

Remote magnetic sensing can be used to monitor the position of objects in real time, enabling ground transport monitoring, underground infrastructure mapping, and hazardous detection. However, magnetic signals are typically weak and complex, requiring sophisticated physical models to analyze them and a detailed knowledge of the system under study, factors that are frequently unavailable. In this work, we provide a solution to these limitations by demonstrating a machine-learning (ML) method that can be trained exclusively on experimental data, without the need of any physical model, to predict the position of a magnetic target in real time. The target can be any object with a magnetic signal above the floor noise, and in this case, we use a quantum diamond magnetometer to track variations of a few hundred nanoteslas produced by an elevator moving along a single axis. The one-dimensional movement is a simple yet challenging scenario, resembling realistic environments such as high buildings, tunnels, or train circuits, and is the first step toward building broader applications. Our ML algorithm can be trained in approximately 40 min, achieving over 80% accuracy in predicting the target’s position at a rate of 10 Hz, for a positional error tolerance of 30 cm, which is well within the the 4-m spacing between parking levels. Our results open up the possibility to apply this ML method more generally for real-time monitoring of magnetic objects, which will broaden the scope of magnetic detection applications.

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References (37)

  1. W. Sun, L. Dai, X. Zhang, P. Chang, and X. He, RSOD: Real-time small object detection algorithm in UAV-based traffic monitoring, Appl. Intell. 52, 8448 (2022).
  2. R. Ke, Z. Li, S. Kim, J. Ash, Z. Cui, and Y. Wang, Real-time bidirectional traffic flow parameter estimation from aerial videos, IEEE Trans. Intell. Transp. Syst. 18, 890 (2017).
  3. R. Kays, M. C. Crofoot, W. Jetz, and M. Wikelski, Terrestrial animal tracking as an eye on life and planet, Science 348, aaa2478 (2015).
  4. G. Gariepy, F. Tonolini, R. Henderson, J. Leach, and D. Faccio, Detection and tracking of moving objects hidden from view, Nat. Photonics 10, 23 (2016).
  5. H. Ghafoor and Y. Noh, An overview of next-generation underwater target detection and tracking: An integrated underwater architecture, IEEE Access 7, 98841 (2019).
  6. Y. Jiang, W. Chen, X. Zhang, X. Zhang, and G. Yang, Real-time monitoring of underground miners’ status based on mine IoT System, Sensors 24, 739 (2024).
  7. Y. Bock and D. Melgar, Physical applications of GPS geodesy: A review, Rep. Prog. Phys. 79, 106801 (2016).
  8. R. B. Langley, Propagation of the GPS signals, in GPS for Geodesy, edited by A. Kleusberg and P. J. G. Teunissen, Lecture Notes in Earth Sciences, Vol. 60 (Springer, Berlin, Heidelberg, 1996), pp. 103–140.10.1007/BFb0117680
  9. H. S. Hewawasam, M. Y. Ibrahim, G. Kahandawa, and T. A. Choudhury, Comparative study on object tracking algorithms for mobile robot navigation in GPS-denied environment, in Proceedings of the IEEE International Conference on Industrial Technology, Melbourne, Australia (IEEE, 2019), pp. 19–26.10.1109/ICIT.2019.8754960
  10. X. Wang, W. Li, B. Moran, B. C. Gibson, L. T. Hall, D. A. Simpson, A. N. Kealy, and A. D. Greentree, Quantum diamond magnetometry for navigation in GNSS denied environments, in Gravity, Positioning and Reference Frames, edited by J. T. Freymueller and L. Sánchez, Vol. 156 (Springer, Cham, 2022), pp. 87–92.10.1007/1345_2023_218
  11. J. Callmer, M. Skoglund, and F. Gustafsson, Silent localization of underwater sensors using magnetometers, EURASIP J. Adv. Signal Process. 2010, 709318 (2010).
  12. N. Wahlström and F. Gustafsson, Magnetometer modeling and validation for tracking metallic targets, IEEE Trans. Signal Process. 62, 545 (2014).
  13. Z. Chen, R. Chen, T. Deng, Y. Wang, W. Di, H. Luo, and T. Han, Magnetic anomaly detection using three-axis magnetoelectric sensors based on the hybridization of particle swarm optimization and simulated annealing algorithm, IEEE Sens. J. 22, 3686 (2022).
  14. A. Soheilian, M. Ranjbaran, and M. M. Tehranchi, Position and direction tracking of a magnetic object based on an Mx-atomic magnetometer, Sci. Rep. 10, 1294 (2020).
  15. X. Wu, S. Huang, M. Li, and Y. Deng, Vector magnetic anomaly detection via an attention mechanism deep-learning model, Appl. Sci. 11, 11533 (2021).
  16. J. Cárdenas, C. Denis, H. Mousannif, C. Camerlynck, and N. Florsch, Magnetic anomalies characterization: Deep learning and explainability, Comput. Geosci. 169, 105227 (2022).
  17. T. Sun, X. Wang, J. Wang, X. Yang, T. Meng, Y. Shuai, and Y. Chen, Magnetic anomaly detection of adjacent parallel pipelines using deep learning neural networks, Comput. Geosci. 159, 104987 (2022).
  18. Y. Wang, Q. Han, G. Zhao, M. Li, D. Zhan, and Q. Li, A deep neural network based method for magnetic anomaly detection, IET Sci. Meas. Technol. 16, 50 (2022).
  19. B. N. J. Vandavasi, H. Venkataraman, and A. R. Gidugu, Machine learning-based electro-magnetic field guided localization technique for autonomous underwater vehicle homing, Ocean Eng. 280, 114692 (2023).
  20. X. Meng, Y. Zhang, X. Zhang, S. Jin, T. Wang, L. Jiang, L. Xiao, S. Jia, and Y. Xiao, Machine learning assisted vector atomic magnetometry, Nat. Commun. 14, 6105 (2023).
  21. Y. Liu, Y. Wu, L. Yang, P. Zhou, J. Kuang, W. Yu, J. Wang, Z. Xu, and G. Li, A Multi-task learning for submarine cable magnetic anomaly recognition, J. Mar. Sci. Eng. 11, 900 (2023).
  22. T.-H. Chiang, Z.-H. Sun, H.-R. Shiu, K. C..-J. Lin, and Y.-C. Tseng, Magnetic field-based localization in factories using neural network with robotic sampling, IEEE Sens. J. 20, 13110 (2020).
  23. P. Li, M. Abdel-Aty, Q. Cai, and Z. Islam, A deep learning approach to detect real-time vehicle maneuvers based on smartphone sensors, IEEE Trans. Intell. Transp. Syst. 23, 3148 (2022).
  24. S. C. Scholten, A. J. Healey, I. O. Robertson, G. J. Abrahams, D. A. Broadway, and J.-P. Tetienne, Widefield quantum microscopy with nitrogen-vacancy centers in diamond: Strengths, limitations, and prospects, J. Appl. Phys. 130, 150902 (2021).
  25. J. M. Schloss, J. F. Barry, M. J. Turner, and R. L. Walsworth, Simultaneous broadband vector magnetometry using solid-state spins, Phys. Rev. Appl. 10, 034044 (2018).
  26. L. Rondin, J.-P. Tetienne, T. Hingant, J.-F. Roch, P. Maletinsky, and V. Jacques, Magnetometry with nitrogen-vacancy defects in diamond, Rep. Prog. Phys. 77, 056503 (2014).
  27. G. J. Abrahams, S. C. Scholten, A. J. Healey, I. O. Robertson, N. Dontschuk, S. Q. Lim, B. C. Johnson, D. A. Simpson, L. C. L. Hollenberg, and J.-P. Tetienne, An integrated widefield probe for practical diamond nitrogen-vacancy microscopy, Appl. Phys. Lett. 119, 254002 (2021).
  28. M. W. Doherty, N. B. Manson, P. Delaney, F. Jelezko, J. Wrachtrup, and L. C. L. Hollenberg, The nitrogen-vacancy colour centre in diamond, Phys. Rep. 528, 1 (2013).
  29. F. Jelezko and J. Wrachtrup, Single defect centres in diamond: A review, Phys. Status Solidi (a) 203, 3207 (2006).
  30. See Supplemental Material at http://link.aps.org/supplemental/10.1103/vjtb-d8kb for additional details on the NV magnetometer calibration procedure and magnetic sensitivity, full experimental dataset, and the ML architecture.
  31. S. F. Ahmed, M. S. B. Alam, M. Hassan, M. R. Rozbu, T. Ishtiak, N. Rafa, M. Mofijur, A. B. M. Shawkat Ali, and A. H. Gandomi, Deep learning modelling techniques: Current progress, applications, advantages, and challenges, Artif. Intell. 56, 13521 (2023).
  32. Y. LeCun, Y. Bengio, and G. Hinton, Deep learning, Nature (London) 521, 436–444 (2015).
  33. Z. Li, F. Liu, W. Yang, S. Peng, and J. Zhou, A survey of convolutional neural networks: Analysis, applications, and prospects, IEEE Trans. Neural Networks Learn. Syst. 33, 6999 (2022).

  34. https://github.com/Fertmeneses/ML_QDM_Meneses_et_al.

  35. K. Sasaki, Y. Monnai, S. Saijo, R. Fujita, H. Watanabe, J. Ishi-Hayase, K. M. Itoh, and E. Abe, Broadband, large-area microwave antenna for optically detected magnetic resonance of nitrogen-vacancy centers in diamond, Rev. Sci. Instrum. 87 (2016).
  36. K. Cabello-Solorzano, I. Ortigosa de Araujo, M. Peña, L. Correia, and J. Tallón-Ballesteros, The impact of data normalization on the accuracy of machine learning algorithms: A comparative analysis, in International Conference on Soft Computing Models in Industrial and Environmental Applications (Springer, Berlin, 2023), pp. 344–353.
  37. D. Singh and B. Singh, Investigating the impact of data normalization on classification performance, Appl. Soft Comput. 97, 105524 (2020).

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Phys. Rev. Applied 26, 054003– Published 2 November, 2026
  • Received 8 December 2025
  • Revised 17 June 2026
  • Accepted 27 August 2026
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