Generally speaking, sensor fusion techniques combine data and knowledge from multiple sources of information to achieve better (less expensive, more accurate, etc.) inferences than those that would be deduced from an individual sensor. Signal processing algorithms for preprocessing sensor data are then needed, together with precise mathematical models (to describe the relation between the sensor outputs and the quantity of interest) and efficient fusion algorithms (to combine the information from the individual sensors). In recent decades, sensor fusion has become an interesting and multidisciplinary topic with applications in several fields, since any task involving estimation problems from multiple sources of information can benefit from the use of sensor fusion methodologies.
Particularly, signal estimation problems in sensor networks constitute a fertile research field with an active progress due to the great number and variety of applications of networked systems in different contexts, such as data acquisition and processing, target tracking and localization, communication, etc. Usually, in practice, network sensors may randomly fail, collapse or suffer communication interferences, so it is necessary to design estimation methods that take into account these random restrictions.
This Special Issue aims at gathering the most recent advances and latest approaches of all topics within the broad field of the fundamentals and applications of sensor fusion and signal processing. Contributions from both theoretical and application sides are welcome, and we also accept survey/tutorial manuscripts.
Potential topics include (but are not limited to):
Signal estimation in sensor networks;
Information fusion techniques and applications;
Fusion estimation algorithms;
Sensor fusion for detection;
Control systems and sensor fusion;
Sensor fusion for automotive applications;
Target tracking, fusion and control;
Signal and image processing.