An improved node localization based on adaptive iterated unscented Kalman filter for WSN

In this paper, by combining iterative strategy and adaptive factor, an adaptive iterated unscented Kalman filter (AIUKF) is presented for the node positioning system in Wireless Sensor Network (WSN), which is based on unscented Kalman filter(UKF). According to the range-based localization algorithm model, RSSI is used to measure distance; an improved method which consisted by maximum likelihood estimation and regional constraint condition is utilized to realize the node initial positioning, and AIUKF is applied to achieve precise positioning finally, meanwhile using RSSI as the measurement values of observation equation directly. The simulation results show that the performance of the proposed positioning algorithm is improved obviously compared with EKF algorithm and UKF algorithm.