摘要
为提高传感器节点自定位精度,满足不同网络规模应用的需要,提出了合成蒙特卡罗定位算法。算法采用混合采样箱方法提高采样成功率,在锚节点密度较低时,先利用多跳锚节点方法收集锚节点信息,然后再运用采样箱方法计算未知节点位置,解决了采样速度慢和锚节点密度低时的应用受限问题。仿真实验结果表明,此算法可以不受锚节点密度限制,且提高了定位精度,具有自适应性和实用性。
Synthesized Monte-Carlo Localization（SMCL） was proposed for improving the accuracy of sensor node self-localization and satisfying the applications need of varied network scale. The Mixture Monte-Carlo Box method was adopted to increase valid sampling rate. When the anchor node density low,Multi-hop anchor node method was utilized to collect anchor nodes information, and then to calculate the position of node with Monte-Carlo Box. SMCL solved slow sampling speed and the constraint of low anchor node density. The simulation results showed that SMCL might overcome the limit of anchor node density and improve the accuracy of node localization. It is self-adaptive and practical.
出处
《衡阳师范学院学报》
2012年第6期63-65,共3页
Journal of Hengyang Normal University
基金
国家自然科学基金资助项目[41171075]
湖南省科技厅资助项目[2011GK3033]
湖南省重点学科建设资助
关键词
移动无线传感器网络
定位
蒙特卡罗
mobile wireless sensor networks
localization
Monte-Carlo
