  A distributed adaptive algorithm for estimation of sparse unknown parameters
in the presence of nonGaussian noise is proposed in this paper based on
normalized least mean fourth (NLMF) criterion. At the first step, local
adaptive NLMF algorithm is modified by zero norm in order to speed up the
convergence rate and also to reduce the steady state error power in sparse
conditions. Then, the proposed algorithm is extended for distributed scenario
in which more improvement in estimation performance is achieved due to
cooperation of local adaptive filters. Simulation results show the superiority
of the proposed algorithm in comparison with conventional NLMF algorithms.
