Sampling design schemes for distributed parameter estimation networks

This paper proposes two sampling schemes for distributed parameter estimation networks. The estimation network comprises a number of remotely located sensors which process the observed signal locally and then convey the processed data to a data fusion centre to make the final estimate of the parameter of interest. In the first sampling scheme, all sensors utilise same number of sampling points, and the distribution of sampling points at each sensor is determined to maximise the estimation accuracy. By contrast, in the second one, the sensors are assigned a different number of sampling points, and the objective is to determine the sampling point assignment which maximises the overall estimation performance given a constraint on the total number of sampling points available. The two sampling design problems are solved by minimising the Fisher information loss. The validity of the proposed sampling design schemes is demonstrated by means of two numerical examples.

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