  The present study proposes incorporating non-parametric knowledge into the
diffusion least-mean-squares algorithm in the framework of a maximum a
posteriori (MAP) estimation. The proposed algorithm leads to a robust
estimation of an unknown parameter vector in a group of cooperative estimators.
Utilizing kernel density estimation and buffering some intermediate
estimations, the prior distribution and conditional likelihood of the
parameters vector in each node are calculated. Pseudo Huber loss function is
used for designing the likelihood function. Also, an error thresholding
function is defined to reduce the computational overhead as well as more
relaxation against noise, which stops the update every time an error is less
than a predefined threshold. The performance of the proposed algorithm is
examined in the stationary and non-stationary scenarios in the presence of
Gaussian and non-Gaussian noise. Results show the robustness of the proposed
algorithm in the presence of different noise types.
