摘要
该文提出一种高精度的移动传感器网络中实现定位跟踪的方法,该方法利用未知节点在运动过程中网络拓扑结构变化的信息提高锚节点利用率,并改善样本采集效率。在无拓扑结构变化的情况下采用牛顿插值方法对节点当前位置进行预测,当拓扑结构有变化时,采用拓扑结构变化构建适应值函数,并用粒子群算法优化样本点的质量。仿真实验结果表明,该文算法与传统算法相比加快了收敛速度,提高了定位精度,改善了在低锚节点密度时的性能。
The paper presents a high precision of localization algorithm in mobile sensor networks. The algorithm utilizes topological change of unknown nodes during the process of node motion to improve utilization of anchor node and the efficiency of sampling. In the case of no topological change,Newton interpolation will be adopted to calculate the current position of the node and when the topology changes,the fitness function will be created with the changes,then the particle swarm optimization will be applied to optimize the sample quality. Simulation result show that the algorithm outperforms the tranditional algorithm in convergence speed,localiization accuracy,and requirement of node density.
出处
《传感技术学报》
CAS
CSCD
北大核心
2015年第4期537-543,共7页
Chinese Journal of Sensors and Actuators
基金
国家自然科学基金项目(61102065
61370003)
广东省科技计划项目(2013B040401014)
广东省自然科学基金项目(S2012040010974)
关键词
无线传感网
拓扑结构
蒙特卡罗算法
粒子群算法
牛顿插值
WSN
changes of topological
monte carlo localization
particle swarm optimization
newton interpolation
