Distributed Adaptive Filtering of $\alpha$ -Stable Signals

A cost-effective framework for distributed adaptive filtering of α-stable signals over sensor networks is proposed. First, the filtering paradigm of α-stable signals through multiple observations made over a network of sensors is revisited and an optimal solution is formulated. Then, an adaptive gradient descent based algorithm for distributed real-time filtering of α-stable signals via multiagent networks is derived. This not only provides an approximation of the formulated optimal solution, but also a cost-effective algorithm that scales with the size of the network. Moreover, performance of the derived algorithm is analyzed and convergence conditions are established.