Sensor and Data Fusion
Often one sensor or one measurement principle can not capture all the information needed to make a desired conclusion. The idea with sensor and data fusion is to use inputs from complementary information sources that when combined, can be used to draw conclusions that can not be obtained from any of the sources alone.
A typical example is the estimation of breaking friction coefficient for air planes. Here, a number of sensor inputs describing the recent history of weather and surface conditions are combined to make a prediction of the breaking friction that is more accurate than today’s methods.
One challenge in sensor fusion is to determine how different measurements of varying qualities are to be combined to give accurate and reliable results. If not properly done a bad measurement can sometimes deteriorate an already good estimate, while in other situations the same measurement may be the only good source of information available. It is thus necessary to take into account which and when the different measurements are relevant and how for instance contradictory information shall be weighted.
For this we use what is available of physical understanding and models of the observed system and the measuring systems, empirical statistical models for the accuracy and reliability of the individual measurements, and multivariate analysis and modeling techniques to combine the information.
Application examples:
If you are interested in more information please contact
.
|
|
|
Sensing module for downhole monitoring of flow in oil production wells. The module contains pressure, temperature, acoustic and capacitive sensors. |