摘要
蒙特卡罗定位算法在无线传感器网络移动节点定位中具有重要的作用.为了提高定位精度,提出了基于序列相关值的蒙特卡罗优化算法SCMCL.以接收的RSSI信号值对移动节点初定位,并将其作为新的采样中心,SCMCL可以优化蒙特卡罗系列算法的采样区域,同时将移动节点收到的锚节点信号值存储为目标序列,通过比较样本序列和目标序列间的相关值来过滤样本点,并将相关值作为加权标准来计算移动节点的坐标.仿真验证,SCMCL算法在相同的锚节点密度和最大速度下和同类算法相比,定位误差均减少了10%左右.
Monte Carlo Localization（MCL）has a decisive role for the mobile nodes’localization in Wireless Sensor Net-works（WSN）.In order to improve the positioning accuracy,an improved algorithm called Sequence Correlation Optimized Monte Carlo Localization（SCMCL）is proposed.Adopting the mobile node’s location based on the received signal strength indicator （RSSI）as the new sampling center,SCMCL can optimize the sampling area of MCL and etc.The signal values are stored as a target sequence,and by comparing the correlation values between samples’sequences and the target sequence,samples can be filtered out. Also the correlation values are used as the weighted standards to calculate coordinates of the mobile nodes.Extensive simulation re-sults confirm that the new localization approach outperforms MCL and etc.The SCMCL algorithm reduces the localization error by about 10% under the same density of beacon nodes and the maximum speed of mobile nodes.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2015年第10期2110-2116,共7页
Acta Electronica Sinica
基金
国家自然科学基金(No.61271125)
河北省自然科学基金(No.F2013205084)
河北省教育厅青年基金(No.Q2012124)
关键词
无线传感器网络
移动节点定位
蒙特卡罗
序列相关值
wireless sensor networks
mobile nodes’localization
Monte Carlo
sequence correlation
