Multi-sensor indoor positioning

Abstract

In this study, multi-sensor indoor positioning methods, which fuse the tri-laterated position data of the target are considered. The lateration is based on the dis tances that are obtained using the signal strengths received from different Wi-Fi access points. A new method, which is based on federated Kalman filtering (FKF) and makes use of the fingerprint data, namely, federated Kalman filter with skipped covariance updating (FKF-SCU) is proposed for indoor positioning. After that chal lenging issue of FKF, information sharing coefficient assignment is studied and two online adaptation methods based on received signal strength indication (RSSI) and distance information gathered from APs are proposed. Lastly, FKF-SCU structure is combined with adaptive FKF configuration. The data collected on two different test beds are used to compare the performance of the proposed positioning methods to those of the regular federated and centralized filters. It is shown on the test data that these algorithms improve the position accuracy and provide fault tolerance whenever signal reception is interrupted from an access point.