DTN中一种基于灰色预测和状态估计的位置预测方法

Node position prediction method based on grey theory and state estimation in DTN

典型的延迟容忍网络(DTN)中,由于中继节点间的链路延时和间歇性中断,用于预测运动节点位置的观测数据在传输中常常滞后、丢失。为了在这样的环境下对这些节点的位置进行及时、准确的预测,首先建立了基于方位角的节点运动状态方程,然后利用灰色预测理论对方位角进行预测,再基于该预测值利用扩展卡尔曼滤波算法对目标节点的坐标值进行递推估计。仿真分析表明,采用这样的位置预测算法,可以克服DTN中观测数据经常缺失的缺点,可实现对DTN节点的位置进行在线预测,与对坐标值进行直接预测相比,其准确性有进一步提高。

In classical delay tolerant network （DTN）, because of the great propagation delay and intermittent link interruption between relay nodes, the observation data for the mobile node＇ s position prediction are often late to reach the computing station, or even dropped in the transmitting procedure. To predict in time and correctly, this paper developed a method, which firstly set up the mobile node＇ s motion state equations based on its azimuth angle, then used the grey theory to predict the angle, and applied the extended Kalman filter to recursively estimate the mobile node＇ s coordinates. The simulation experiment resuhs show that this method can cope with the data-dropping environment of DTN, and work in a timely online mode. Compared to the method of direct grey prediction, it also gives more correct results.