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Optimal multirate filtering with its application in estimation of the current of a transformer

This paper presents a real time multirate multisensor state fusion estimation algorithm, where data from multisensors are fused without interpolation or augmentation of state or measurement dimensions, in which different sensors may have different sampling rates and may encounter missing measurements. The state model is formulated at the highest sampling rate, and the measurement equations for each sensor are known. The generated state estimation algorithm is optimal in the sense of linear minimum variance, and the simulation on the estimation of the current of a simple two-coil transformer illustrate its' feasibility and effectiveness.