Multi-Sensor Information Fusion

Dear Colleagues,

Information fusion techniques 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. These 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 and has been applied in many fields, such as geospatial information systems, business intelligence, oceanography, discovery science, intelligent transport systems, and wireless sensor networks, etc. Recently, thanks to the vast developments in senor and computer memory technologies, more and more sensors are being used in practical systems and a large amount of measurement data is recorded and restored, which may actually be "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 classical multi-sensor information fusion technique can effectively deal with a limited amount of sensor data, and can even obtain optimal results in real time. However, regarding "big series time data", we have to consider how to deal with the mass of sensor data in real-time processes, and how to model the multi-sensor system based on the huge amount of data, etc. The development of sensor systems has created many new challenges in multi-sensor information fusion theory and its application. Therefore, the innovations of information fusion still need to be studiously pursued in future research works.

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.

Prof. Xue-Bo Jin
Dr. Yuan Gao
Collection Editors

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