Advances in Multi-Sensor Information Fusion: Theory and Applications 2017
Hong Wei (Ed.)
Feng-Bao Yang (Ed.)
Shuli Sun (Ed.)
Xue-Bo Jin (Ed.)
The information fusion technique can integrate a large amount of data and knowledge representing the same real-world object and obtain a consistent, accurate and useful representation of that object. The data may be independent or redundant, and can be obtained by different sensors at the same time or at different times. A suitable combination of investigative methods can substantially increase the profit of information in comparison with that from a single sensor. Multi-sensor information fusion has been a key issue in sensor research since the 1970s and it has been applied in many fields, such as geospatial information systems, business intelligence, oceanography, discovery science, intelligent transport systems, wireless sensor networks, etc. Recently, thanks to the vast development in sensor and computer memory technologies, more and more sensors are being used in practical systems and a large amount of measurement data are recorded and restored, which may actually be the "time series big data". For example, sensors in machines and process control industries can generate a lot of data, which have real, actionable business value. The fusion of these data can greatly improve productivity through digitization. The goal of this Special Issue is to report on innovative ideas and solutions for the methods of multi-sensor information fusion in the emerging applications era, focusing on development, adoption and applications.