Data fusion is an important emerging area that is growing rapidly due to the fact that it provides means for combining pieces of information coming from different sources and sensors.
In respect to separate sensors/sources, the objective of data fusion is to improve overall system performance, including:
- Improved decision making
- Increased detection capabilities
- Diminished number of false alarms
- Improved reliability.
Different data fusion methods have been developed in order to optimize the overall system output in a variety of applications for which data fusion might be useful: security (humanitarian, military), medical diagnosis, environmental monitoring, remote sensing, robotics, etc.
You could say the concept of sensor fusion attempts to replicate the capability of the central nervous system to process sensory inputs from multiple sensors simultaneously. For robotic devices, for example, feedback from one sensor is typically not enough, particularly for implementation of control algorithms. Sensor fusion can be used to compensate for deficiencies in information by utilizing feedback from multiple sensors.
The deficiencies associated with individual sensors to calculate particular types of information can be compensated for by combining the data from multiple sensors. The net effect of sensor fusion is that the resulting information should have less uncertainty than would have occurred if the sensors were used individually.
Sensor fusion can also help to compensate for sensor noise, limited accuracy, failure, or a lack of information about a particular aspect of the environment. In addition, sensor fusion can be used when the ideal sensor of choice is cost prohibitive.
Sensor fusion can utilize information directly from sensors or historic sensor data or can use indirect information from prior knowledge about the system inputs.
One particularly exciting area of use for sensor fusion is in the manufacture of autonomous vehicles. Sensor fusion is a critical requirement in creating an autonomous vehicle’s “brain,” ensuring it can make intelligent, accurate and timely decisions based on behaviors of other traffic participants. Sensor fusion takes the inputs of different sensors and sensor types and uses the combined information to perceive the environment more accurately. This results in better and safer decisions than independent systems can achieve.
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