This paper presents a method for fusing measurement samples from multiple sensors into a dependable robust estimation of a variable in the control environment. Each sensor measurement is represented by a measurement value and a confidence marker that corresponds to the respective variance of the measurement. We propose the Confidence-weighted Averaging (CWA) algorithm for fusing measurements with respect to the estimated variance of the measurement error. For calibrated sensors with uncorrelated error functions this algorithm is optimal for producing a result with minimum mean squared error.
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