Diffusion adaptation is a powerful strategy for distributed estimation and learning over
networks. Motivated by the concept of combining adaptive filters, this work proposes a …

This paper is concerned with the distributed filtering problem for a class of discrete-time
stochastic systems over a sensor network with a given topology. The system presents the …

This paper presents weighted diffusion least mean p-power (LMP) algorithm for distributed
estimation of an unknown sparse vector in a sensor network. We consider a network, in …

This work provides a comprehensive overview of adaptive diffusion networks, from the first
papers published on the subject to state-of-the-art solutions and current challenges. These …

In this article, we consider the distributed direct target tracking using the received radio
signal by exploiting time delay and Doppler for heterogeneous wireless sensor networks …

In this paper, we deal with distributed estimation problems in diffusion networks with
heterogeneous nodes, ie, nodes that either implement different adaptive rules or differ in …

This paper proposes a novel diffusion subband adaptive filtering algorithm for distributed
networks. To achieve a fast convergence rate and small steady-state errors, a variable step …

This paper deals with the development of robust diffusion strategy for wireless sensor
networks using minimum-Wilcoxon-norm. The Wilcoxon norm based robust estimation now …

In this paper, we analyze diffusion strategies in which all nodes attempt to estimate a
common vector parameter for achieving distributed estimation in adaptive networks. Under …

The diffusion least-mean-square (dLMS) algorithms have attracted much attention owing to
its robustness for distributed estimation problems. However, the performance of such …