移动机器人辅助下基于GM-CKF的无线传感器网络节点定位研究

针对无线传感器网络(WSNs)节点定位问题,提出一种移动机器人辅助作用下,融入高斯混合容积卡尔曼滤波(GM-CKF)优化的节点定位方法。将移动机器人与WSNs结合,发挥两者的特点和优势,充分利用机器人的机动性及无线传感器节点的可计算性,设计并仿真了一种机器人-节点、节点-节点协作的节点定位方式,并利用带有门限判别和选择性高斯分割的GM-CKF算法,对目标节点的预估位置实施预测修正。仿真结果表明,所提出的移动机器人与WSNs协作定位方法实现了对节点的定位估计,GM-CKF算法的融合有效提高了定位的精度和稳定性。

Research on node localization of wireless sensor networks based on GM CKF assisted by mobile robot

Aiming at the problem of the node localization of wireless sensor networks (WSNs),an optimized node localization method based on the assistant function of the mobile robot is proposed,which is integrated into the Gaussian mixture cubature Kalman filter (GM-CKF).The combination of mobile robots and WSNs gives play to both of their advantages,which makes full use of mobility of robots as well as calculability of wireless sensor’s node.A collaborative robot-node and node-node localization is designed and simulated.Also the estimated position of target node is predicted and corrected through CM-CKF with threshold discrimination and selective Gaussian segment.The simulation result shows that the method proposed in this paper implements node ’s position estimation,and the integration of GM-CKF efficiently improves the accuracy and the stability of localization.