一种无信标无线传感器网络中的目标定位策略

Target localization scheme for cluster-based beaconless WSN

无信标无线传感器网络的传感器节点通常是按照一定的概率,以分组形式部署,为实现其定位和动态节点跟踪,提出了的无信标定位发现策略,在已有的部署之上,建立模型去反映目标位置和监测传感器探测信息之间的内在关系,同时还建立了预测模型来对目标移动方式进行推断。利用贝叶斯理论构造了一个条件概率分布,将以上两种模型相关参数归并起来,并在这个分布上应用最大似然估计(MLE)方法来估测目标的位置。实验结果表明此目标定位策略取得了较好的效果。

A beacon-less location discovery scheme was proposed due to the toUowing observations： sensors are usually deployed in groups and sensors from the same group may land in different locations that follow a probability distribution. With this prior deployment knowledge, a model was built to reflect inherence information between positions of the object and observations provided by alert sensors. Also a prediction modal was set up to offer prior knowledge about moving patterns of the object. Generally speaking, the Bayes rule provides us a suitable way to combine those two kinds of knowledge so as to generate a conditional distribution. By applying the Maximum Likelihood Estimation （MLE） method to this distribution, the object＇s accurate positions could be estimated. Experimental results prove that the scheme can get better effect.