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

This paper is concerned with estimation of multiple frequencies fromincomplete and/or noisy samples based on a low-CP-rank tensor data model whereeach CP vector is an array response vector of one frequency. Suppose that it isknown a priori that the frequencies lie in some given intervals, we developefficient super-resolution estimators by exploiting such prior knowledge basedon frequency-selective (FS) atomic norm minimization. We study the MDVandermonde decomposition of block Toeplitz matrices in which the frequenciesare restricted to lie in given intervals. We then propose to solve the FSatomic norm minimization problems for the low-rank spectral tensor recovery byconverting them into semidefinite programs based on the MD Vandermondedecomposition. We also develop fast solvers for solving these semidefiniteprograms via the alternating direction method of multipliers (ADMM), where eachiteration involves a number of refinement steps to utilize the prior knowledge.Extensive simulation results are presented to illustrate the high performanceof the proposed methods.

Multi-dimensional Spectral Super-Resolution with Prior Knowledge via Frequency-Selective Vandermonde Decomposition and ADMM