Robust Nonlinear Distributed Estimation Using Maximum Correntropy

With the development of information theoretical learning, maximum correntropy criterion (MCC) has shown its utility in non-Gaussian information approximation. The MCC has been applied in Gaussian filters to provide robust estimation under non-Gaussian environment. The extension of MCC to its information form enables robust distributed estimation. In this paper, a new MCC based diffusion information filter is developed for distributed multiple sensor estimation. Non-Gaussianity due to nonlinear dynamics and measurement can be accounted for by incorporating both state estimation error and measurement uncertainty into the correntropy. A numerical example is used to demonstrate the effectiveness of the proposed MCC based diffusion information filter.

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