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Calibration and estimation of redundant signals

This paper presents an adaptive filter for real-time calibration of redundant signals consisting of sensor data and/or analytically derived measurements. The measurement noise covariance matrix is adjusted as a function of the a posteriori probabilities of failure of the individual signals. An estimate of the measured variable is obtained as a weighted average of the calibrated signals. The weighting matrix is recursively updated in real time instead of being fixed a priori. The filter software is presently hosted in a Pentium platform and is portable to other commercial platforms. The filter can be used to enhance the Instrumentation and Control System Software in large-scale dynamical systems.