Total least squares (TLS) is a popular solution technique for overdetermined systems of
linear equations, where both the right-hand side and the input data matrix are assumed to …

In this paper, the problem of adaptive distributed learning in diffusion networks is
considered. The algorithms are developed within the convex set theoretic framework. More …

Distributed algorithms are developed for optimal estimation of stationary random signals and
smoothing of (even nonstationary) dynamical processes based on generally correlated …

This paper considers gossip distributed estimation of a (static) distributed random field (aka,
large-scale unknown parameter vector) observed by sparsely interconnected sensors, each …

In this paper, we consider distributed estimation of a noise-corrupted deterministic
parameter in energy-constrained wireless sensor networks from energy-distortion …

Distributed averaging describes a class of network algorithms for the decentralized
computation of aggregate statistics. Initially, each node has a scalar data value, and the goal …

We introduce a distributed adaptive algorithm for linear minimum mean squared error
(MMSE) estimation of node-specific signals in a fully connected broadcasting sensor …

The paper studies distributed static parameter (vector) estimation in sensor networks with
nonlinear observation models and noisy intersensor communication. It introduces separably …

In this paper, we develop algorithms for distributed computation of averages of the node
data over networks with bandwidth/power constraints or large volumes of data. Distributed …

In this paper, we revisit an earlier introduced distributed adaptive node-specific signal
estimation (DANSE) algorithm that operates in fully connected sensor networks. In the …