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Existing localization algorithms which use Kalman filter for indoor environment fix noise covariance or change noise covariance by complicated filters. Since localization system does not reflect real environment around it, fixed noise covariance makes localization system unpractical. Also complicated filters limit the applications which localization algorithms can apply. In this paper, we propose a method that adaptively changes noise covariance. The proposed method recognizes changes of moving pattern based on results of localization, and then adjusts the noise covariance. In addition, the method varies localization period to catch up with changed mobile nodes's movement rapidly and reduce overhead caused by calculation of location when mobile node moves regular pattern. Simulation result from MATLAB shows the proposed method outperformed an algorithm which has constant localization period and noise covariance of Kalman filter by 30%.