基于FARIMA模型的无线网络状态融合方法

摘要

为了解决无线网络传输过程中受噪音干扰的问题,提出了一种新的信号状态融合算法(signal fusion based on wavelet transform and date association,SFDF).该算法针对小波变换和卡尔曼滤波刻画的缺陷,利用数据关联和FARIMA模型对信号进行了有效融合,并设计长相关信号的算法流程.同时以分形布朗运动(fractional Brownian motion,FBM)模型产生实验数据,深入研究了信号状态与干扰因素之间的关系.实验结果表明,与以往算法相比,SFDF更加具有适应性.%In order to mitigate the interference noise in wireless network transmission, a novel signal status fusion algorithm (signal fusion based on wavelet transform and date association, SFDF) is proposed. In this algorithm, the signals are effectively fused with date association and FARIMA model for the shortage of wavelet transform and Kalman filter at first, and the algorithm processes of long-range dependence signal is presented. Then, with the experiment data of fractional Brownian motion model, a simulation is conducted to research on the relationship between the signal status and influencing factors. The result shows that, compared with previous algorithms, it is more adaptive for SFDF.