A robust self-calibrating data fusion architecture

The authors present a general mathematical framework for the fusion of noisy sensor data. On the basis of the mathematical theory of dynamical systems they couple the outputs of the sensors to obtain a nonlinearly averaged overall estimate of the physical quantity to measure which automatically discards outliers from the averaging process. Drifts within the time series of single sensors can be compensated through a recalibration by the method of time scale inversion. By means of a unified way of representing information as stable states of a dynamical system it is possible to integrate different sorts of information such as expert knowledge and sensor information smoothly within the data fusion system. They verify the feasibility of their approach on the basis of simulated stochastic data sets and on the basis of data from a study in which the brightness temperature of oil films on sea water was measured. The proposed self-calibrating sensor fusion architecture extends the work they presented at IGARSS '99 in Hamburg (1999).