On the fusion of inertial data for signal strength localization

Localization in wireless sensor networks (WSN) based on the evaluation of received signal strength (RSS) values of communication packets has received a considerable amount of research interest in the last years. In spite of the number of approaches that have been presented, the performance of most systems remains rather poor due to the unpredictable behavior of RSS values and the resulting fluctuating position estimations. In this paper, an approach to cope with these fluctuations for the purpose of ad-hoc person localization in WSN is presented. A hip-mounted inertial measurement unit (IMU), carried by the person to localize, is used to collect data on the current movement. This data is used to stabilize the RSS position estimation by means of a Kalman filter. The experimental evaluation shows that the proposed method carries the potential to improve RSS localization methods. We present results of experiments in in- and outdoor environments and quantify the improvements that can be achieved by fusing inertial data with RSS position estimation schemes for person localization. It can be concluded that fusing inertial data can improve the performance of RSS-based localization systems.