Research on Indoor Positioning Based on Geomagnetic Periodicity

摘要

Location based service (LBS) has attracted increasing attention due to the rapid development of mobile internet in recent years. Geomagnetic field as an infrastructure-free resource has been noted by LBS experts. However, the accuracy of indoor positioning based on geomagnetic field still needs to be further improved. Therefore, this paper puts forward a novel algorithm in view of geomagnetic periodicity and its own characteristics. In order to make full use of the spatiotemporal characteristics of geomagnetic field, Empirical Mode Decomposition (EMD) is introduced to decompose the preprocessed samples into stable and variable components that will be standardized for eliminating the differences in three dimensions (X, Y, Z). After that, Multiple Regression Model (MRM) for each station will be stepwise constructed based on the average values of the standardized components at different periods, which are expressed by Mean Generation Matrix (MGM) as independent variables. To further improve the positioning accuracy, four candidate stations with the predicted values, which are gotten from the constructed models, closest to the test sample are picked out to analyze their features for determining the optimal positioning result. The proposed algorithm and traditional K-Nearest Neighbor (KNN) method using different series of samples are compared on account of correct positioning times and accuracy, the experimental results demonstrate that the proposed algorithm is feasible and efficient.