Location Accuracy Indicator Enhanced Method Based on MFM/PDR Integration Using Kalman Filter for Indoor Positioning

Based on the complementary advantages of integrated positioning methods, the multisource integrated positioning technology has become one of the mainstream technologies in the field of indoor navigation and positioning. However, little literature is available on the estimating for positioning accuracy of different sources in the integration. The negative benefits of integrated positioning method are caused by inaccurately estimated accuracy, which frequently occur in two sources integrated positioning without any correction. To study how to estimate the positioning accuracy of different sources in the integration, we propose a location accuracy indicator enhanced method based on magnetic field matching (MFM) and pedestrian dead reckoning (PDR) integration using Kalman filter (KF): the prior covariance based on PDR and the posterior covariance based on MFM are adopted to calculate noise matrixes of the KF and established the location accuracy indicator enhanced filtering system. From the results, the proposed method, which was less affected by changing the matching range of the magnetic field and broadening the interval of the fingerprint database, obtained 2.746 and 1.881 m for 90% error and root-mean-square error (RMSE), respectively, and the error was 24%–27% lower than the best empirical model, including weighted nearest ${k}$ -neighborhood (WKNN)/PDR (3.776 and 2.523 m) and integrated weight nearest neighborhood (IWNN)/PDR (3.712 and 2.485 m). It was verified by the experiments that the proposed method could accurately calculate noise matrixes of the KF and provide accurate weights, having stronger positioning performance and robustness than the others.