Low-Complexity Gridless 2D Harmonic Retrieval via Decoupled-ANM Covariance Reconstruction

Yu Zhang, Yue Wang, Zhi Tian, G. Leus, Gong Zhang

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

This paper aims at developing low-complexity solutions for super-resolution two-dimensional (2D) harmonic retrieval via covariance reconstruction. Given the collected sample covariance, a novel gridless compressed sensing approach is designed based on the atomic norm minimization (ANM) technique. The key is to perform a redundancy reduction (RR) transformation that effectively reduces the large problem size at hand, without loss of useful frequency information. For uncorrelated sources, the transformed 2D covariance matrices in the RR domain retain a salient structure, which permits a sparse representation over a matrix-form atom set with decoupled 1D frequency components. Accordingly, the decoupled ANM (DANM) framework can be applied for super-resolution 2D frequency estimation, at low computational complexity on the same order of the 1D case. An analysis of the complexity reduction of the proposed RR-D-ANM compared with benchmark methods is provided as well, which is verified by our simulation results
Original languageEnglish
Title of host publication28th European Signal Processing Conference (EUSIPCO 2020)
Place of PublicationAmsterdam (Netherlands)
PublisherEurasip
Pages1876-1880
Number of pages5
ISBN (Electronic)978-9-0827-9705-3
Publication statusPublished - 1 Aug 2020
EventEUSIPCO 2020: The 28th European Signal Processing Conference - Amsterdam, Netherlands
Duration: 18 Jan 202122 Jan 2021
Conference number: 28th

Conference

ConferenceEUSIPCO 2020
CountryNetherlands
CityAmsterdam
Period18/01/2122/01/21
OtherDate change due to COVID-19 (former date August 24-28 2020)

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