Modeling and Interpolation of the Ambient Magnetic Field by Gaussian Processes

Arno Solin, Manon Kok, Niklas Wahlstrom, Thomas B. Schon, Simo Sarkka

Research output: Contribution to journalArticleScientificpeer-review

61 Citations (Scopus)
180 Downloads (Pure)


Anomalies in the ambient magnetic field can be used as features in indoor positioning and navigation. By using Maxwell's equations, we derive and present a Bayesian nonparametric probabilistic modeling approach for interpolation and extrapolation of the magnetic field. We model the magnetic field components jointly by imposing a Gaussian process (GP) prior to the latent scalar potential of the magnetic field. By rewriting the GP model in terms of a Hilbert space representation, we circumvent the computational pitfalls associated with GP modeling and provide a computationally efficient and physically justified modeling tool for the ambient magnetic field. The model allows for sequential updating of the estimate and time-dependent changes in the magnetic field. The model is shown to work well in practice in different applications. We demonstrate mapping of the magnetic field both with an inexpensive Raspberry Pi powered robot and on foot using a standard smartphone.

Original languageEnglish
Pages (from-to)1112-1127
JournalIEEE Transactions on Robotics
Issue number4
Publication statusPublished - 2018
MoE publication typeA1 Journal article-refereed


  • Gaussian process (GP)
  • Magnetic field
  • Mapping
  • Maxwell's equations
  • Online representation


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