Multi-sensor Data Fusion Algorithm For Wireless Sensor Networks

Multi-sensor data fusion is a process of multi-level and multi-aspects,and during this process,multi-source data is examined,combined,correlated,estimated and assembled to get an accurate estimation of state and identity.This paper conducted in-depth analysis and research on data fusion algorithm on data layer in wireless sensor networks.At first,the background knowledge of wireless sensor networks and data fusion are introduced including the association between them.And various kinds of data fusion algorithm are summarized.Then some mathematical models of common data fusion algorithm are introduced,which are steppingstone to research on algorithm.The common data fusion algorithms and each features are summarized,including each advantages and disadvantages.Then make relative research and analysis on the maximum and minute close degree in fuzzy mathematics,introducing it into data fusion and redefine the correlated characteristic of statistics.On the basis of this,calculate the close degree matrix,mean value and variance of close degree.Redefine and recalculate the weight factor and finally get the expression of fusion result.Use experiment statistics and simulation platform respectively to do performance analysis on the arithmetic,and compare it with others.Comparing results shows good accuracy and robustness.While there is not an accepted fair method to screen sampled data with the mainstream data fusion algorithm.This article estimates the average discrete degree by means of interquartile range in statistical mathematics.Using it as standard,statistics higher than it are seen as big error statistics and delete them from sample set.After this,we introduced subordinating degree function in fuzzy mathematics to describe the correlation of statistics and calculated the fusion matrix.At last calculate the associated fusion digree and balanced fusion degree to confirm the final weight coefficient.Similarly,use experiment statistics and simulation platform respectively to do performance analysis and comparison on the the performance of the algorithms.Comparing result shows good accuracy and robustness.