On distance estimation based on radio propagation models and outlier detection for indoor localization in Wireless Geosensor Networks

The determination of a precise position in Wireless Geosensor Networks (GSN) requires the use of e.g. distance measurements. These distance observations derived by Received Signal Strength (RSS) measurements are inherently inaccurate. Furthermore, in general, the distance observations using RSS do not take obstacles into account. In this work in progress paper we present a new approach and first simulations to correct erroneous RSS measurements affected by obstacles in indoor scenarios. This technique is combined with the known “Anomaly Correction in Localization” (ACL) algorithm where sensor measurements are used to improve the determined sensor node positions and to detect and to eliminate outliers. Therefore, the new “extended Anomaly Correction in Localization” algorithm (eACL) will be formulated.