Abstract
This paper describes a method for location estimation of mobile wireless local area network (LAN) clients in multistory buildings using the strength of the received signals in a state space framework. Data pertaining to the physical positions of personal electronic devices or mobile robots are important for information services and robotic applications. We focus on integrating the estimation results with other sensor data based on a state space framework. The estimation model for location provides a variance of a mobile client’s location. We integrate the estimation results and the motion results of the mobile client using a Kalman filter. The estimation model is re-initialized when the mobile client moves to another floor in the building by detecting the change in the floor number where the mobile client has moved. This is done by using the Bayesian inference. Experimental results show the feasibility of this method.
