Efficient Super-Resolution Two-Dimensional Harmonic Retrieval Via Enhanced Low-Rank Structured Covariance Reconstruction

Conference paper (2020)

Authors

Yue Wang George Mason University
Yu Zhang George Mason University, Nanjing University of Aeronautics and Astronautics
Zhi Tian George Mason University
G.J.T. Leus Signal Processing Systems - EEMCS
G. Zhang Nanjing University of Aeronautics and Astronautics
Research Group
Signal Processing Systems (EEMCS) (TU Delft)
Copyright: © 2020 Yue Wang, Yu Zhang, Zhi Tian, G.J.T. Leus, Gong Zhang
DOI
help_outline
https://doi.org/10.1109/ICASSP40776.2020.9054756
Super-resolution 2D harmonic retrieval D-ANM MMV LRSCR
More Info
expand_more

Published Date

2020

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Signal Processing Systems

Abstract

This paper develops an enhanced low-rank structured covariance reconstruction (LRSCR) method based on the decoupled atomic norm minimization (D-ANM), for super-resolution two-dimensional (2D) harmonic retrieval with multiple measurement vectors. This LRSCR-D-ANM approach exploits a potential structure hidden in the covariance by transferring the basic LRSCR to an efficient D-ANM formulation, which permits a sparse representation over a matrix-form atom set with decoupled 1D frequency components. The new LRSCR-D-ANM method builds upon the existence of a generalized Vandermonde decomposition of its solution, which otherwise cannot be guaranteed by the basic LRSCR unless a very conservative condition holds. Further, a low-complexity solution of the LRSCR-D-ANM is provided for fast implementation with negligible performance loss. Simulation results verify the advantages of the proposed LRSCR-D-ANM over the basic LRSCR, in terms of the wider applicability and the lower complexity.