Optimal Fusion of Sensors.

In complex plants it will often be necessary to use measurements from a large number of sensors in order to get sufficient information about the plant. In general some of the sensor data will be supplementary and some will be complementary. The task of fusioning these measurements is therefore both the task of extracting as much information from the sensors as possible, but also to utilize the redundancy by taking into account the uncertainties and characteristics of the measurements so that the information extracted will be as reliable and complete as possible. How this task should be performed is the topic of this project. Focus has been put on the Kalman filter. Given a model of the plant, the sensors and the noise affecting the system the Kalman filter is capable of combining the observations in an optimal way when a number of prerequisites regarding the linearity of the model and the nature of the noise is met. In most applications where the Kalman filter is used the prerequisits are not met and the filter will generally not be optimal. The consequences of this and how to cope with these is another of the main focuses of this project.

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Last Updated at Tue Nov 19 1997
Thomas Larsen