Research On Data Fusion Estimation Algorithms In Wearable Body Network

As one of the important components of Internet of Things(Io T),wearable body network(WBN)has various of applications in military,medical,industrial,and other fields.In recent years,the rapid development of electronics industry and big data-related technologies has put forward higher requirements on the accuracy of information in WBN.On one hand,data fusion can further improve the accuracy and availability of data by using the redundant information of original data.On the other hand,the data estimation can remove or reduce the influence of noise according to the state information of the original data.Therefore,high precision data fusion estimation technology in WBN data processing is one of the research hotspots.WBN mainly collect and process movement data and status data of individuals or groups.Because of the limitations of the equipment and surrounding environment,noise is mixed into the data,which makes the data uncertain.Therefore,reducing uncertainty or making full use of uncertainty information to improve data accuracy is an important research topic in this field.Besides,after data collection and processing,further data analysis is needed to obtain the final and usable conclusion.Therefore,it is another important research in this field to combine data fusion estimation with subsequent data analysis and make full use of data information.In this paper,combined with the actual problems in WBN data processing,the high-precision fusion estimation technology is studied from the above two perspectives respectively:(1)The emergence of non-stationary processes would lead to a certain delay and a significant decrease in the accuracy when calculating the estimation results.To solve the above problems,an Unscented Kalman Filter-based perceptual fusion estimation model for emergencies is proposed.The model introduces the absolute difference threshold to discover the non-stationary process of data flow,and adjusts the model parameters adaptively when the non-stationary process occurs,which avoids the problem of data accuracy decline caused by the non-stationary process to some extent.The simulations show that the non-stationary phase has little influence on the fusion estimation accuracy of the proposed model.Moreover,the classification accuracy of KNN algorithm based on this model is improved by at least 4%.(2)The clustering accuracy of the possible world-based algorithm declines rapidly or even fails when the uncertainty difference of the collected data is large.To solve this problem,a fusion estimation model based on possible worlds and K-L divergence is proposed.The model treats the estimated results as probability distribution and makes full use of the uncertainty of the data.Simulation analyses of simulated data and real data show that the model improves the clustering accuracy,avoids the failure of clustering algorithm,and extends the application range of clustering algorithm when there is a big difference in the uncertainty of data collected.(3)When there is a certain correlation between the collected data in the wearable network,the related algorithms are of high complexity and low estimation accuracy.To avoid above situation,a attractor based quadratic fusion estimation model is proposed.The model uses cluster attractor to approximate the unknown relationship in state data,obtains the numerical relation expression between the state data.And then uses this expression to carry on the second processing to the fusion estimation result,further enhances the accuracy of data fusion estimation.Simulation analyses show that the model can improve the precision of data under certain conditions and make the distribution of data in the same cluster more centralized.(4)The fusion estimation algorithm based on Kalman's idea cannot deal with the data under normal distribution without noise,and the current processing energy consumption based on reduction and data discretization is too large.To solve this problem,a mixed H₂/H?-based lightweight fusion estimation model is proposed.The model uses mixed H₂/H?estimation algorithm for fusion estimation,which weakens the requirements of the model on noise.At the same time,considering the limited energy in wearable devices,the data compression method is used to reduce the data transmission consumption.The simulation analyses show that the model improves the overall estimation accuracy,the traffic of each device in the model is lower than that of other similar models,and maintains strong robustness when the noise changes dramatically.