In this paper we tackle distributed detection of a localized phenomenon of interest (POI)
whose signature is sparse via a wireless sensor network. We assume that both the position …

State-space models (SSMs) are a powerful statistical tool for modelling time-varying systems
via a latent state. In these models, the latent state is never directly observed. Instead, a …

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 …

In this paper, we consider the problem of detection of sparse stochastic signals with
quantized measurements in sensor networks. The observed sparse signals are assumed to …

In this brief, a novel adaptive filtering algorithm for block-sparse system identification called,
Block-Sparse Adaptive Bayesian Algorithm (BS-ABA) is proposed. We use a Gaussian …

We consider the problem of detection of sparse stochastic signals with a distributed sensor
network. Multiple sensors in the network are assumed to observe sparse signals, which …

We consider a distributed detection problem within a wireless sensor network (WSN), where
a substantial number of sensors cooperate to detect the existence of sparse stochastic …

This paper proposes robust diffusion maximum versoria criterion algorithms to enhance the
performance of the distributed estimation in a network of agents under impulsive noise …

This paper presents a quaternion block sparse representation (QBSR) method for structural
sparse signal recovery in quaternion space. Due to the noncommutativity of quaternion …

This paper presents two novel Bayesian learning recovery algorithms for block sparse
signals corresponding to two distinct cases. In the first case, the signals within each block …