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Indoor Localization Scheme Using Magnetic Map for Smartphones

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Abstract

The particle filter algorithm for geomagnetic matching has been combined with Pedestrian Dead-Reckoning (PDR) for indoor positioning, but the current particle filter algorithm still has the problems of blindness and diversity. In this paper, we propose a matching algorithm based on particle filter and gait detection, which can effectively reduce the blindness of particles and increase the diversity of particles. Firstly, we improve the gait detection algorithm based on accelerometer. Secondly, we use the improved complementary filtering algorithm to obtain the user's heading information. Thirdly, the results of gait and heading plus some errors are used as the state model of particle filter. Finally, particles are regenerated through the state model when every time a gait is detected. In this way, the generation position of particles is controlled by the error of gait detection so that the blindness of particles is reduced, and the diversity of particles is guaranteed by regenerating particles every iteration. We use two kinds of magnetic map based on magnetic field vector and magnetic field intensity, and develop an Android application to test the algorithm. The experimental results show that the average positioning accuracy of the proposed method is 0.52 m using magnetic map composed of magnetic field vector, which is better than other methods.

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Acknowledgements

This work was partially supported by Shaanxi provincial fund of China (Grant numbers 2020JM-185).

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Authors and Affiliations

  1. Institute of Electronic CAD, Xidian University, Xi’an, China

    Ling-Feng Shi, Rui He & Bao-Lin Feng

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  1. Ling-Feng Shi
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  2. Rui He
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  3. Bao-Lin Feng
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Correspondence to Ling-Feng Shi.

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Shi, LF., He, R. & Feng, BL. Indoor Localization Scheme Using Magnetic Map for Smartphones. Wireless Pers Commun 122, 1329–1347 (2022). https://doi.org/10.1007/s11277-021-08951-w

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