科研之友
微信
新浪微博
Facebook
分享链接
摘要
The localization of sensor node is an essential problem for many economic forecasting applications in wireless sensor networks. Considering that the mobile sensors change their locations frequently overtime, Monte Carlo localization algorithm utilizes the moving characteristics of nodes and employs the probability distribution PDF) in the previous time slotto estimate the current location by using a weighted particle filter. However, it also has the problem of insufficient number of valid samples,which further affects the node's localization accuracy. In this paper,differential evolution method is introduced into the Monte Carlo localization algorithm. The sample weight is taken as the objective function, and differential evolution algorithm is implemented in sample stage. Finally,the node position is estimated by making the sample close to the actual location of the node instead of being filtered out. The simulation results demonstrate that the proposed algorithm provides a better position estimation with less localization error.
