无人机协助下基于SR-CKF的无线传感器网络节点定位研究

针对无线传感器网络(WSN)节点的实际应用场合大多数分布在复杂的三维地形,并且当无线传感器网络分布规模达到一定程度时,对每一个传感器节点装载 GPS模块来实现节点定位不切实际的情况,提出了一种无人机(UAV)协助下利用极大似然估计法(MLE)对未知节点进行初步定位,引入平方根容积卡尔曼滤波(SR-CKF)算法对未知节点进行精确定位,采用阈值选择的更新策略来减小非线性因素的影响。仿真结果表明:所提出的UAV-WSN-MLE-SRCKF协作定位方式实现了三维地形中未知传感器节点的定位估计,大量减少了装载GPS模块所带来的成本,同时也提高了定位精度和稳定性。

Node localization of wireless sensor networks based on SR-CKF assisted by unmanned aerial vehicles

Most applications of wireless sensor network nodes are distributed in the complex 3D terrain. When the wireless sensor network distribution scale reaches a certain extent, realizing the node positioning by loading the global positioning system (GPS) module on every sensor node becomes impractical. In view of this situation, this paper puts forward a kind of unmanned aerial vehicle (UAV)-assisted maximum likelihood estimation (MLE) method for the preliminary positioning of unknown nodes. We introduce the square root cubature Kalman filtering (SRCKF) algorithm for the precise positioning of unknown nodes and use the threshold selection update strategy to reduce the influence of nonlinear factors. The simulation results show that the UAV-WSN-MLE-SRCKF collaboration localization method proposed in this paper realizes the location estimation of unknown sensor nodes in the 3D terrain, reduces the cost of loading GPS modules to a large extent, and simultaneously improves the positioning accuracy and stability.