Multi-Sensor Indoor Positioning

Abstract

In this paper, the performance of three different kinds of Kalman Filter (KF) structure: Single Kalman Filter (SKF)(which filters data of a single sensor), Centralized Kalman Filter (CKF) and Federated Kalman Filter (FKF) are studied considering the indoor positioning problem. Kalman filters are widely used in outdoor positioning and navigation applications providing good results. For multi-sensor applications, KF has centralized and decentralized structures. In this study, multisensor and single-sensor dedicated Kalman filter structures FKF, CKF and SKF are applied in indoor positioning problem by taking its outdoor navigation performance into consideration. Received Signal Strength (RSS) data are generated, from which distance information, a vital part of the simulations, are obtained. Three different noise levels are used to assess performance of filters. Results gathered via different simulations showed that multi-sensor structures provide a better solution than single sensor structures.