RSSI-based Indoor Mobile Localization in Wireless Sensor Network

Abstract Location techniques play an important part in wireless sensor network. The received signal strength indicator (RSSI) based localization is a promising technique since it requires relatively low configuration and energy. The received signal strength is mainly influenced by propagation environment in wireless sensor network. So we proposed an indoor mobile location algorithm (IMLA). We firstly introduce a signal propagation model. And then we employ the least squares and maximum likelihood estimation to estimate the parameters of signal model. Finally the extended Kalman filter is used to filter the RSSI values and convert the measured RSS value to distance. And the Cramer-Rao bound for RSSI-based location estimation is expressed. Simulation results show that the proposed IMLA outperforms the maximum likelihood location and non-filter mobile location algorithm with better location estimation accuracy.