Distributed Adaptive LMF Algorithm for Sparse Parameter Estimation in Gaussian Mixture Noise
This work addresses sparse parameter estimation in distributed systems with non-Gaussian noise, representing an incremental improvement over existing NLMF algorithms.
The paper tackled the problem of estimating sparse parameters in non-Gaussian noise by proposing a distributed adaptive algorithm based on the normalized least mean fourth criterion, which improved convergence rate and reduced steady-state error compared to conventional methods.
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.