In this paper

an indoor positioning technology based on Kalman filter algorithm is proposed to solve the problem that the standard deviation of forecasted user location is larger

and the move of user location is not smooth at real-time tracking stage.Firstly

nearest neighbor method is utilized to forecast the location of user

then the Kalman filter is used to filter the forecasted user location in order to further improve the performance and stability of indoor positioning system.Experimental results illustrate that the proposed kalman filter can increase the 85% accuracy of estimation with a precision of 2 meters to 93%

and increase 95% accuracy of estimation with a precision of 3 meters to 98%.Therefore

the proposed Kalman filter algorithm in this paper can improve the positioning accuracy remarkably and stably.