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

The mathematical theory of super-resolution developed recently by Cand\`{e}sand Fernandes-Granda states that a continuous, sparse frequency spectrum can berecovered with infinite precision via a (convex) atomic norm technique given aset of uniform time-space samples. This theory was then extended to the casesof partial/compressive samples and/or multiple measurement vectors via atomicnorm minimization (ANM), known as off-grid/continuous compressed sensing (CCS).However, a major problem of existing atomic norm methods is that thefrequencies can be recovered only if they are sufficiently separated,prohibiting commonly known high resolution. In this paper, a novel (nonconvex)sparse metric is proposed that promotes sparsity to a greater extent than theatomic norm. Using this metric an optimization problem is formulated and alocally convergent iterative algorithm is implemented. The algorithmiteratively carries out ANM with a sound reweighting strategy which enhancessparsity and resolution, and is termed as reweighted atomic-norm minimization(RAM). Extensive numerical simulations are carried out to demonstrate theadvantageous performance of RAM with application to direction of arrival (DOA)estimation.

Enhancing Sparsity and Resolution via Reweighted Atomic Norm Minimization