Sensor Data Fusion: Techniques and Applications

Dear Colleagues,

The ever-accelerating technological evolution and the deepening of the computer and language development techniques expand our concept of the "intelligent machine", able to play its role in an adequate, repetitive, and precise manner, using a network of sensors and transducers. The process able to find direct interaction and exploit better knowledge of the inspected area whose characteristics are acquired by the selected sensors, properly analyzed by an IT system to obtain consistent and usable results, is commonly referred to as the Data Fusion problem. Currently there are different types of sensors: cameras, magnetic, infrared, sound and ultrasound microphones, accelerometers, gyroscopes, Global Positioning Systems, Radar, sonar, and so on. The meaning of “Data Fusion” is extremely complex because, with a simple term, it includes skills and tools from various fields, such as statistical analysis, multidimensional signal processing, complex systems, control theory, and machine learning. Data Fusion gains significance in applications where a large amount of data and information is selected and merged to produce an appropriate quality and integrity of the information that will be used to implement decisions. Main fields of such problems deal with many different fields of application, such as automotive, robotics, decision support systems, smart roads, and surveillance. Depending on the specific application at hand and on the goals and specificity of the required precisions and resolutions, the Data Fusion problem has never had a unique possible solution, and the definition of techniques and methods to properly extract correlations between data acquired by different sensors is always a challenging problem, often dealing with a large amount of heterogeneous data. The problems of processing speed, efficiency, power saving, and timely accurate results need to be addressed. The scientific literature presents several possible data fusion techniques, both heuristic and data driven, each using different scenarios, technologies, and approaches, even for the same specific application playground. This Special Issue proposal aims to collect several techniques and methods trying to give a broad and interesting perspective of the general problem of Data Fusion.

Dr. Cataldo Guaragnella
Guest Editor

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• Data Fusion
• multidimensional data
• complex sensing systems
• RGB-D cameras and stereo vision
• multicamera systems
• 3D point clouds
• automotive
• robotics
• scene reconstruction
• multisensor feature extraction
• radar
• sonar
• lidar
• acoustic sensors
• IR cameras
• accelerometers
• GPS and in/outdoor positioning systems
• multidimensional data processing
• magnetic sensors