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Smartphone based indoor positioning has become a hot topic in pervasive computing, because of the need to improve indoor location-based services. In order to strengthen positioning accuracy, researchers have tried to leverage high-resolution magnetic fingerprint with particle filter and dynamic time warping (DTW). These approaches are computation-hungry, which increases hardware cost for positioning companies. By analyzing magnetic features for pedestrian users, we present a mass-centered weight update scheme to decrease calculation overheads. Finally, the proposed positioning algorithm is tested in a realistic situation, showing high-quality localization capability.