Abstract
The resolution of geomagnetic field is low. In geomagnetic indoor positioning in large-scale scenes, with the increase of the number of fingerprints, the geomagnetic characteristic values corresponding to different position coordinates will have more similarities, resulting in a higher probability of fingerprint positioning mismatch. To solve this problem, Spline interpolation, RBF interpolation and Kriging interpolation are applied to the construction of geomagnetic fingerprint database in the offline stage, and BP neural network model is used for matching and positioning in the online stage. The experimental results show that the geomagnetic fingerprint database constructed by RBF interpolation can effectively improve the positioning accuracy and stability.
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