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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

This paper focuses on a networked state estimation problem for a spatiallylarge linear system with a distributed array of sensors, each of which offerspartial state measurements, and the transmission is lossy. We propose ameasurement coding scheme with two goals. Firstly, it permits adjusting thecommunication requirements by controlling the dimension of the vectortransmitted by each sensor to the central estimator. Secondly, for a givencommunication requirement, the scheme is optimal, within the family of linearcausal coders, in the sense that the weakest channel condition is required toguarantee the stability of the estimator. For this coding scheme, we derive theminimum mean-square error (MMSE) state estimator, and state a necessary andsufficient condition with a trivial gap, for its stability. We also derive asufficient but easily verifiable stability condition, and quantify theadvantage offered by the proposed coding scheme. Finally, simulations resultsare presented to confirm our claims.

Multi-sensor State Estimation over Lossy Channels using Coded Measurements