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Zhang, Yu (author), Wang, Yue (author), Tian, Zhi (author), Leus, G.J.T. (author), Zhang, Gong (author)
This paper proposes a super-resolution harmonic retrieval method for uncorrelated strictly non-circular signals, whose covariance and pseudo-covariance present Toeplitz and Hankel structures, respectively. Accordingly, the augmented covariance matrix constructed by the covariance and pseudo-covariance matrices is not only low rank but also...
conference paper 2023
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Wang, Yue (author), Zhang, Yu (author), Tian, Zhi (author), Leus, G.J.T. (author), Zhang, Gong (author)
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...
conference paper 2020
document
Zhang, Yu (author), Wang, Yue (author), Tian, Zhi (author), Leus, G.J.T. (author), Zhang, Gong (author)
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)...
conference paper 2020