基于卡尔曼滤波的WSNs节点定位研究

节点定位是无线传感器网络中的关键技术之一。在采用装备有GPS装置的移动信标-移动机器人、无人机的基础上,将加权最小二乘估计与扩展卡尔曼滤波(EKF)组合,进行未知节点定位。算法首先利用加权最小二乘估计(WLSE),获得无线传感器网络未知节点的初步位置,再用扩展卡尔曼滤波进一步提高定位精度。并且提出了加权因子的确定方法,同时,算法还提出了移动信标位置参与EKF迭代计算的最优排序方案。算法可以实现传感节点的低成本定位,可以达到较高的定位精度。仿真结果显示,算法与目前常用的最小二乘估计相比,未知节点的定位精度有较大的提高。算法应用RSSI测距方式,它还可应用于TDOA,TOA等基于测距的定位算法中,具有较普遍的应用意义。

Research on Node Localization Based on Kalman Filter for WSNs

Localization of nodes was a key technology for application of wireless sensor network.A hybrid localization algorithm(HLA) was proposed based on mobile beacons,such as mobile robots and UVA,which were equipped with GPS for wireless sensor networks.The hybrid algorithm combines weighted least squares estimation with Extended-Kalman Filter(EKF).Firstly,the algorithm got an inaccurate position coordination of node by using weighted least squares estimation.Then the algorithm achieved an accurate localization of node by using Extended-Kalman Filter.An approach was proposed to determine the weight exponent.And an optimal sorting solution of beacon position was proposed for EKF iteration.This algorithm was low-cost.Simulation showed that the hybrid localization algorithm could achieve higher accurate than least squares estimation.In this paper,the algorithm was based on RSSI.In fact,the algorithm could be applied to other localization algorithm which was based on range-based method,such as TDOA,TOA.So it could be applied to many applications.