基于AIUKF的WSN节点定位算法∗

摘要

In this paper,by combining iterative constraint conditions and adaptive factor,an adaptive iterated un￣scented Kalman filter( AIUKF) algorithm is presented to improve the node localization accuracy in Wireless Sensor Network( WSN),which is based on the unscented Kalman filter( UKF) . According to the model of range￣based lo￣calization algorithm,RSSI is used to measure the distance;the maximum likelihood estimation method is utilized to realize node initial location;by using AIUKF algorithm to achieve the accurate positioning of the node finally and RSSI is applied as the measurement values of observation equation directly. The simulation results show that the proposed algorithm based on AIUKF has the best positioning precision than EKF algorithm and UKF algorithm.%针对无线传感器网络节点定位精度不足的问题，在无迹卡尔曼滤波( UKF)的基础上，结合迭代约束条件和自适应因子，提出了一种自适应迭代无迹卡尔曼滤波( AIUKF)算法。根据基于测距的节点定位模型，采用RSSI进行测距，以极大似然估计法进行节点初步定位，利用AIUKF算法对节点进行精确定位，并且直接以RSSI作为系统的观测量。仿真结果表明，本文提出的基于AIUKF的定位算法相比EKF和UKF算法具有更高的定位精度。