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The present study proposes incorporating non-parametric knowledge into thediffusion least-mean-squares algorithm in the framework of a maximum aposteriori (MAP) estimation. The proposed algorithm leads to a robustestimation of an unknown parameter vector in a group of cooperative estimators.Utilizing kernel density estimation and buffering some intermediateestimations, the prior distribution and conditional likelihood of theparameters vector in each node are calculated. Pseudo Huber loss function isused for designing the likelihood function. Also, an error thresholdingfunction is defined to reduce the computational overhead as well as morerelaxation against noise, which stops the update every time an error is lessthan a predefined threshold. The performance of the proposed algorithm isexamined in the stationary and non-stationary scenarios in the presence ofGaussian and non-Gaussian noise. Results show the robustness of the proposedalgorithm in the presence of different noise types.

Robust Non-parametric Knowledge-based Diffusion Least Mean Squares over Adaptive Networks