The goal of this paper is to propose a distributed adaptive algorithm for estimation of sparse unknown parameters in the presence of nonGaussian noise. The proposed adaptive algorithm uses zero norm for accelerating the speed of convergence in sparse conditions and it uses fourth order of instantaneous error to access lower steaty state error in the presence of nonGaussian noise. Then, a distributed version of this algorithm is developed in which cooperation of local adaptive filters provides spatial diversity that results more reduction in steady state error. Simulation results show the superiority of the proposed algorithm in comparison with other algorithms.

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