English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We construct team-optimal estimation algorithms over distributed networks forstate estimation in the finite-horizon mean-square error (MSE) sense. Here, wehave a distributed collection of agents with processing and cooperationcapabilities. These agents observe noisy samples of a desired state through alinear model and seek to learn this state by interacting with each other.Although this problem has attracted significant attention and been studiedextensively in fields including machine learning and signal processing, all thewell-known strategies do not achieve team-optimal learning performance in thefinite-horizon MSE sense. To this end, we formulate the finite-horizondistributed minimum MSE (MMSE) when there is no restriction on the size of thedisclosed information, i.e., oracle performance, over an arbitrary networktopology. Subsequently, we show that exchange of local estimates is sufficientto achieve the oracle performance only over certain network topologies. Byinspecting these network structures, we propose recursive algorithms achievingthe oracle performance through the disclosure of local estimates. For practicalimplementations we also provide approaches to reduce the complexity of thealgorithms through the time-windowing of the observations. Finally, in thenumerical examples, we demonstrate the superior performance of the introducedalgorithms in the finite-horizon MSE sense due to optimal estimation.

Team-Optimal Distributed MMSE Estimation in General and Tree Networks