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Minimum entropy approach for multisensor data fusion

In this paper, we present a minimum entropy fusion approach for multisensor data fusion in non-Gaussian environments. We represent the fused data in the form of the weighted sum of the multisensor outputs and use the varimax norm as the information measure. The optimum weights are obtained by maximizing the varimax norm of the fused data. The minimum entropy fusion solution only depends on the empirical distribution of the sensor data and makes no specific distribution assumptions about the sensor data. Numerical simulation results are provided to show the effectiveness of the proposed fusion approach.