Focusing Matching Localization Method Based on Indoor Magnetic Map

With the development of Internet of Things (IoT) technology, people’s demand for indoor location-based services is growing. Indoor magnetic field has a wealth of fingerprint information that can be combined with position stamps to construct an indoor magnetic map (IMM). Localization scenarios based on IMM include personnel attendance, equipment tracking, and fire emergency, etc. However, the localization technology is complicated, the localization accuracy based on IMM is generally low, and the localization result lacks credibility due to the fluctuation of indoor magnetic field. Therefore, it is urgent to study a novel localization method in terms of the tradeoff among complexity, accuracy and credibility, which is the purpose of this article. First, the Euclidean distance constraint (EDC) with a variable search radius is proposed to make a rough estimate of the entity’s location. Then the iterative interpolation method (IIM) is proposed to refine the local-IMM, and the multi-magnetic- fingerprint-fusion (MMFF) is proposed to match the magnetic fingerprint based on the refined local-IMM. In short, the proposed method is called focusing matching localization method by combining coarse estimation with fine estimation, which can be abbreviated as EDC-IIM-MMFF. Finally, the effectiveness of the proposed localization method is verified by a series of experiments. The tests show that the root mean square error (RMSE) of location solved by the proposed method is less than 0.3 meters, and the 99% cumulative distribution function (CDF) of location error is less than 0.5 meters, and the complexity is lower than the existing methods, such as Gaussian process regression (GPR), extended Kalman filter (EKF), particle filter (PF), etc.