Multidimensional Sensor Data Fusion Processing System Based on Big Data

Abstract

In order to process data fusion, the author proposes a multidimensional sensor data fusion processing system based on big data. The author discusses the principle and basic steps of multidimensional sensor data fusion and analyzes the classification and common data fusion methods of data fusion. Then, the structure and training process of the DBN algorithm are emphatically expounded, experiments are carried out on the randomly collected multidimensional sensor datasets through the DBN algorithm, the validity of the algorithm is verified, and the algorithm is evaluated. The experimental results show that the number of hidden layers is 100, the number of nodes is 100, the weight matrix is a matrix of 784×100, the learning rate is 2, the momentum is 0.5, the number of samples is 100, and the iteration is 1 time. The average reconstruction error obtained by the MATLAB Deep Learn Toolbox is 65.7798. Conclusion. The method proposed by the author can effectively process multidimensional sensor data fusion.