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Improving Magnetic Flux Density Fingerprint Map Matching by Mitigating AC-Induced Variability
* 1 , 1 , 2 , 2 , 1
1  University College London (UCL)
2  Defence Science and Technology Laboratory (Dstl)
Academic Editor: Guy Buesnel

Abstract:

Magnetic flux density (MFD) fingerprint map matching is a technique which can provide absolute position solutions by comparing a series of MFD measurements with a database [1–4]. The standard technique of fingerprint map matching relies on consistent measurement of the same physical phenomena during the survey (mapping) and the location phases [4]. However, fluctuations in MFD due to AC electricity, influenced by dynamic power requirements like the operation of a 3 KW kettle, challenge standard fingerprint map matching (Figure 1).

This paper addresses this challenge by employing spectral filtering to isolate MFD from AC sources in both the database creation and navigation phases. The resulting isolation of this temporal variable source enhances the base map's resilience to changes over time, ensuring its functionality regardless of the building's power status. This is particularly vital for applications such as firefighting operations, where the base map's effectiveness is crucial irrespective of the presence or absence of AC mains current.

The experimental environment for this paper is outdoors, which enables full control of the magnetic environment, in which a simulation of unshielded underfloor cables is used (Figure 2). The experiment involves a single AC source alongside multiple DC sources (Figure 3). This filtering technique should work anywhere and future work could bring this technique indoors. By using a variety of different magnetometers with different sampling rates the effectiveness of the filtering is analysed.

In conclusion, this study enhances MFD fingerprint map matching by effectively isolating AC-induced variability, setting the stage for increased robustness of this technique for future applications in indoor navigation.

References

  1. Akai N, Ozaki K. 3D magnetic field mapping in large-scale indoor environment using measurement robot and Gaussian processes. In: 2017 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2017 [Internet]. Sapporo, Japan: IEEE; 2017 [cited 2023 Apr 10]. p. 1–7. Available from: http://ieeexplore.ieee.org/document/8115960/
  2. Ashraf I, Kang M, Hur S, Park Y. MINLOC: Magnetic Field Patterns-Based Indoor Localization Using Convolutional Neural Networks. IEEE Access [Internet]. 2020 [cited 2021 May 25];8:66213–27. Available from: https://ieeexplore.ieee.org/document/9056514/
  3. Ashraf I, Zikria Y Bin, Hur S, Park Y. A Comprehensive Analysis of Magnetic Field Based Indoor Positioning with Smartphones: Opportunities, Challenges and Practical Limitations. IEEE Access [Internet]. 2020 [cited 2022 Sep 13];8:228548–71. Available from: https://ieeexplore.ieee.org/document/9301308/
  4. Hanley D, Oliveira ASD de, Zhang X, Kim DH, Wei Y, Bretl T. The Impact of Height on Indoor Positioning With Magnetic Fields. IEEE Trans Instrum Meas [Internet]. 2021 [cited 2023 Mar 29];70:1–19. Available from: https://ieeexplore.ieee.org/document/9354181/
  5. IET (Institution of Engineering and Technology). Requirements for Electrical Installations: IET Wiring Regulations (18th Edition) [Internet]. 18th ed. 2022. Available from: https://electrical.theiet.org/bs-7671/about-bs-7671/

Keywords: Magnetic; Flux; Density; Map; Matching; Positioning; Alternating; Current; Filtering; Magnetometer

 
 
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