LAUSR

In distributed wireless sensor networks, geographically distributed sensors cooperate wirelessly with each other. While sensing from the environment, the signals from these sensors are often contaminated by noise. Traditional diffusion algorithms for distributed estimation consider this noise to be Gaussian in nature. However, in practice this noise can also be non-Gaussian, which leads to deterioration in performance of traditional adaptive algorithms. Moreover, the parameter vector to be estimated may be sparse in nature. To improve adaptive filter performance for distributed networks, we propose a set of sparsity aware diffusion adaptive filters which are robust to non-Gaussian noises. Extensive simulation study for different Gaussian and non-Gaussian noise environments show the improved estimation ability of the proposed algorithms for modelling highly, moderate and non-sparse distributed systems.