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 consider the problem of resolving closely spaced point sources in onedimension from their Fourier data in a bounded domain. Classical subspacemethods (e.g., MUSIC algorithm, Matrix Pencil method, etc.) show greatsuperiority in resolving closely spaced sources, but their computational costis usually heavy. This is especially the case for point sources with amulti-cluster structure which requires processing large-sized data matrixresulted from highly sampled measurements. To address this issue, we propose afast algorithm termed D-MUSIC, based on a measurement decoupling strategy. Wedemonstrate theoretically that for point sources with a known clusterstructure, their measurement can be decoupled into local measurements of eachof the clusters by solving a system of linear equations that are obtained byusing a multipole basis. We further develop a subsampled MUSIC algorithm todetect the cluster structure and utilize it to decouple the global measurement.In the end, the MUSIC algorithm was applied to each local measurement toresolve point sources therein. Compared to the standard MUSIC algorithm, theproposed algorithm has comparable super-resolving capability while having amuch lower computational complexity.

A measurement decoupling based fast algorithm for super-resolving point sources with multi-cluster structure