基于灰色模型和改进Chan的海洋移动传感器定位

Ocean Mobile Sensor Node Localization Based on GM (1,1) and Improved Chan

摘要:

为了实现海洋水下传感器网络中的移动节点定位,并改善传统节点定位方法在应用于无人值守和复杂环境的海洋生态监控中具有的定位误差大的问题,提出了一种基于灰色模型预测和改进Chan算法的海洋移动传感器节点定位方法；首先,采用灰色模型对节点在下一时刻的采集数据进行预测,然后将预测值与实际采集值进行比较从而判断出移动节点的状态是否正常；在此基础上,采用改进的Chan算法对处于正常状态的移动节点进行定位,从而提高水下移动传感器节点的定位精度；在Matlab中进行仿真实验,实验结果表明:文中方法能在节点运动速度增加、通信半径变大和锚节点密度增加的情况下,均具有比其它方法更低的节点定位误差,具有一定的优越性。 

Abstract:

In order to accomplish the localization for mobile nodes in underwater Sensor Networks, and solve the localization error of the traditional nodes localization method when applied to the compound ocean monitoring without people guarding, a sensor node localization method based on GM (1,1) model and improved Chan is proposed. Firstly, the GM (1,1) model is used to predict the data sensed by the node in the next time, and the prediction value is compared with the data sensed by the node, so they are compared to justify the node is at the normal state. Then, the improved Chan algorithm is used to localize the mobile sensor node with the normal state, so the localization accuracy of the underwater sensor node can be improved. The simulation experiment is implemented in the Matlab, The experiment result shows the method in this paper always has the lower localization error when appears the increment of the sensor node moving speed, communication radius and anchor node density, so it has big priority.