Distributed diffusion nonnegative LMS algorithm over sensor networks

Since most distributed estimation algorithms only try to achieve high estimation precision while ignoring the positive-negative problem of components in the true parameter, estimation using these methods may be physically absurd and uninterpretable. In order to avoid erroneous results, we need to add a nonnegative constraint on the parameter to be estimated. In this paper, we propose a novel distributed diffusion nonnegative LMS algorithm with regularization for estimating some specific parameter. The algorithm keeps the non-negativity of all components in the parameter in the adaptation process. Simulations results illustrate the advantage of our algorithm in the low steady MSD level and high convergence rate.