无线传感器网络中移动节点的后验信号滤波定位法

Posterior Signal Filtering Method for Mobile Nodes Localization in Wireless Sensor Networks

无线传感器网络中移动节点定位面临着高精度和实时性的要求,针对蒙特卡洛定位算法MCL的不足,提出了一种信号滤波改进算法:后验信号滤波法PSFM。通过跟踪未知节点,有效利用最新观测信号,PSFM提取前后时刻共能感知的锚节点的信号范围,并筛除仅前一时刻的锚节点信号范围的样本点,重新设置并优化滤波区域,提高了定位算法的精度。新算法还提出了运用最大似然估计法对样本信息处理,推导移动节点的位置坐标。理论分析和仿真表明新算法和传统MCL算法相比,对节点的部署密度和移动速度有较低的敏感度,表现出良好的算法稳定性。在不同的锚节点密度下定位误差减少了46%～65%,运行时间减少了26%～45%。

A novel signal filtering method which is called posterior signal filtering method（PSFM） is proposed here for improving the Monte Carlo localization（MCL）. The new method can localize the mobile nodes in real time and with high accuracy. By tracking the unknown nodes and using the latest observed signals, PSFM extracts the one-hop anchors that can hear the unknown nodes directly not only at the prior moment but also at the posterior moment. Then it takes the intersection of their signals＇ region with the initial forecast region and excluds the samples that can only hear the prior moment anchors for resetting the filter region. Also Maximum Likelihood Estimation Method is used to derive the mobile node＇s position. Theoretical analysis and experiments show that the new method has lower sensitivity to the deployment density and nodes＇ speed,it improves the localization accuracy by 46% -65% and reduces the running time by 26% -45% compared with the tradition MCL algorithm.