Neural fuzzy based indoor localization by Kalman filtering with propagation channel modeling

In this study, an indoor localization based on the received signal strength indication (RSSI) in wireless sensor networks (WSN) is proposed. The presented approach proceeds in two phases: the first phase is based on the recorded received signal strength at the certain location. The interpolation, curve fitting and an adaptive neural fuzzy inference system (ANFIS) are used to develop the indoor propagation model, respectively. Thus the strength of the received radio signal can be converted to a physical distance approximately; in the second phase, based on the available distances from the positions localized in the test bed are estimated by using an extended Kalman filter (EKF). In comparison among the propagation models based on the interpolation, ANFIS and curve fitting, the experimental results show that the proposed approach provides a precise performance.