A Wireless Localization Algorithm Based on Strong Tracking Kalman Filter

Classical extended Kalman filter algorithm is often used to obtain dynamic estimation of nodes’ position in wireless localization. However, it is prone to generate error accumulation in the filtering process, and lead to filter divergence, which causes low accuracy. The paper explores a strong tracking extended Kalman filter with algorithm a fading factor, which can adjust the gain K in real time, so as to ensure the adaptive adjustment of the new information sequence, as well as the dynamic tracking capability in indoor wireless localization. The experimental results show that the strong tracking extended Kalman filter algorithm has a better tracking capability on dynamic targets, leading to higher tracking accuracy and, smaller absolute error