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针对无线传感器网络中,以蒙特卡罗为基础的移动传感节点定位算法在定位精度和采样效率方面的不足,提出一种DV-Hop辅助的改进蒙特卡罗盒定位算法.通过利用DV-Hop方法获得节点间的真实距离来建立更加精确的锚盒;引入节点随机运动模型,获得节点真实运动速度来优化采样区域,提高定位精度;根据样本到一跳、两跳锚节点的估计距离和真实距离的差值来动态赋予样本不同的权值,提高采样效率.仿真结果表明,当锚节点和未知节点都移动时,所提出算法的定位精度和采样效率与同等条件下的蒙特卡罗盒算法相比均有所提高.
Aiming at the shortage of positioning accuracy and sampling efficiency of the mobile sensor node localization algorithm based on Monte Carlo in wireless sensor networks, a DV-Hop-aided improved Monte Carlo box localization algorithm is proposed. By using DV-Hop The method obtains the real distance between nodes to build a more accurate anchor box. By introducing a stochastic motion model of node, the real velocity of node is obtained to optimize the sampling area and improve the positioning accuracy. According to the sample one-hop, the estimated distance and true And the difference of the samples to dynamically assign different weights to the sample and improve the sampling efficiency.The simulation results show that when the anchor nodes and the unknown nodes move, the proposed algorithm’s positioning accuracy and sampling efficiency are the same as the Monte Carlo box algorithm Compared to have improved.