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

Conference paper (2020)

Authors

Yu Zhang Nanjing University of Aeronautics and Astronautics
Yue Wang George Mason University
Zhi Tian George Mason University
G.J.T. Leus Signal Processing Systems - EEMCS
Gong Zhang Nanjing University of Aeronautics and Astronautics
Research Group
Signal Processing Systems (EEMCS) (TU Delft)
Copyright: © 2020 Yu Zhang, Yue Wang, Zhi Tian, G.J.T. Leus, Gong Zhang
DOI
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https://doi.org/10.23919/Eusipco47968.2020.9287680
2D harmonic retrieval D-ANM RR transformation Covariance reconstruction Low complexity
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Published Date

2020

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Signal Processing Systems

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