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

Conference Paper (2020)
Author(s)

Yue Wang (George Mason University)

Yu Zhang (Nanjing University of Aeronautics and Astronautics, George Mason University)

Zhi Tian (George Mason University)

Geert J.T. Leus (TU Delft - Signal Processing Systems)

Gong Zhang (Nanjing University of Aeronautics and Astronautics)

Research Group
Signal Processing Systems
Copyright
© 2020 Yue Wang, Yu Zhang, Zhi Tian, G.J.T. Leus, Gong Zhang
DOI related publication
https://doi.org/10.1109/ICASSP40776.2020.9054756
More Info
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Publication Year
2020
Language
English
Copyright
© 2020 Yue Wang, Yu Zhang, Zhi Tian, G.J.T. Leus, Gong Zhang
Research Group
Signal Processing Systems
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
Pages (from-to)
5720-5724
ISBN (print)
978-1-5090-6632-2
ISBN (electronic)
978-1-5090-6631-5
Reuse Rights

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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.

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