Trends of Topics and Methods in Data Fusion Research postprint

Abstract: ［Purpose/significance］Data fusion is an important way to realize multi-source data value.Comprehensive analysis of the overall topics of global data fusion research has an important scientific and technological information value for the current data fusion research. ［Method/process］The hot topics and research methods of 16053 literatures from Web of Science core collections were analyzed by word-frequency and co-word analysis. ［Result/conclusion］The data fusion research has shown a significant growth trend, and after more than 30 years development, core research hotspots and methods of data fusion have been formed. In the research, the data fusion of sensors （including wireless sensors） is the core research direction in this field. Fault diagnosis, remote sensing, security and smart grid are the hotspots of the data fusion scenario. Kalman Filter, Neural Network, Dempster-Shafer Evidence Theory and Machine Learning（including Deep Learning, Support Vector Machine, etc.） are the main methods in data fusion, and the synergy network of methods have been formed in data fusion.