Research On The Data Fusion Algorithm Of Network-based Multi-MEMS

With the rapid development of Control Theory, Information Theory, MicroelectronicTechnology, Computer Technology, Network Technology and Sensor Technology,multi-sensor data fusion technology has an extremely wide use in the domain of militaryand civilian, using multi-sensor data fusion technology to solve the problems of exploration,tracking and target recognition, can improve system reliability and robustness, enhance datareliability, improve accuracy, extend the system time and space coverage, and increase thesystem’s real-time performance and information utilization. Through the multi-level,multifaceted and multi-level processing of the data from multiple sensors, we can obtainmore meaningful information which can not be provided by a single sensor and accurateinformation, decision-making basis for a variety of application system can be provided.Therefore, data fusion service has become one of the most important applications in thesensor network. This article focused on researching the following areas:Firstly， after studying the theoretical knowledge of multi-sensors data fusiontechnology, the general design of the Multi-MEMS data fusion algorithm based on networkis presented of the paper. According to data fusion charts and the processing hierarchy of thedata established in the paper, the Multi-MEMS data fusion algorithm based on network is akind of feature level fusion of hybrid integration structure. Then focusing on the data fusionmethod used in this project, the data fusion algorithm based on relative distance and datafusion algorithm based on double base points are proposed in this paper. For noise of dataprocessing, this paper adapts the Kalman filtering algorithm.Secondly, in order to realize the proposed data fusion algorithm, the paper uses VC++and MATLAB hybrid programming to develop the data fusion system software ofNetwork-based Multi-MEMS. The software based on the network of Multi-MEMS datafusion system encapsulates the entire process of the algorithm to process and prove offlinedata and online data. Next the Matlab curve acquired by the analysis and processing ofoffline data intuitively shows the data fusion is reliable and effective, and also proves thefeasibility, reliability, and validity of the data fusion algorithm.Finally, from the experimental results, the accuracy of MIMU measurement data getsgreatly improved after fuse by the proposed algorithm. Also the result proves the data fusionalgorithm is the feasible, reliable, and valid.