Fault Tolerant Indoor Positioning Based on Federated Kalman Filter

Abstract

In this article, multi-sensor indoor positioning, which is based on fusing tri-laterated position data of the target, is considered. A novel method, which is based on federated Kalman filtering and makes use of the fingerprint data, namely, federated Kalman filter with skipped covariance updating (FKF-SCU) is proposed. The data collected on two test beds are used in comparing the performances of the proposed algorithm and that of the regular federated filter. It is shown that the proposed algorithm provides fault tolerance and quick recovery, whenever signal reception from an access point is interrupted, as well as an improvement of 12.57% on the position accuracy.