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提出一种利用临时锚节点的蒙特卡罗箱定位算法.该算法是基于蒙特卡罗定位方法之上,通过引入节点平均速率来获取临时锚节点,并利用一跳范围内的临时锚节点构建最小锚盒、增强样本过滤条件,从而加速了采样和样本过滤.此外,在样本的获取上采用了非随机采样的均衡采样方法,有效地降低了采样次数.仿真结果表明:该算法同蒙特卡罗定位算法等相比,提高了节点的定位精度,降低了节点的能耗.
This paper proposes a Monte Carlo localization algorithm using temporary anchor nodes.The algorithm is based on the Monte Carlo localization method and obtains the temporary anchor nodes by introducing the average node rate and uses the temporary anchor nodes in one hop to construct the minimum Anchor box to enhance the sample filtering conditions, thus speeding up the sampling and sample filtering.In addition, the sample is obtained using a non-random sampling equalization sampling method, effectively reducing the number of samples.The simulation results show that: the algorithm with Monte Carlo Compared with the positioning algorithm, it improves the positioning accuracy of nodes and reduces the energy consumption of nodes.