%0 Journal Article
%A HOU Jian-jun
%A LIU Ying
%A SU Jun-feng
%A ZHU Ming-qiang
%T Indoor Localization Estimation Based on Scaled Iterated Unscented Kalman Filter
%D 2013
%R 10.13190/jbupt.201304.66.049
%J Journal of Beijing University of Posts and Telecommunications
%P 65-70
%V 36
%N 4
%X <p>To improve the precision of received signal strength indicator(RSSI)wireless localization in traditional indoor positioning technology, using simplex sampling and covariance correction, a method of coordinate position and channel parameter simultaneous estimation is presented based on iterated unscented Kalman filter (IUKF) algorithm. Due to the complexity of indoor environment, there exists a big noise in RSSI signal, so the raw data is calibrated by using kernel smoother. The RSSI localization problem is conveyed into the optimal estimation problem of nonlinear equations. Simulation indicates that SIUKF algorithm has higher estimation accuracy compared with extended Kalman filter (EKF) and unscented Kalman filter (UKF). It shows strong robustness, and the computational complexity is appropriated. The accuracy up to 0.65m is obtained with the proposed method and can meet the needs of indoor positioning.</p>
%U https://journal.bupt.edu.cn/EN/10.13190/jbupt.201304.66.049