Reweighted covariance fitting based on nonconvex Schatten-\(p\) minimization for gridless direction of arrival estimation

DOI10.1155/2020/3012952zbMATH Open1544.94163MaRDI QIDQ6534404

Khaldoun Khorzom, Oumayma Al-Dakkak, Lama Zien Alabideen

Publication date: 6 May 2021

Published in: Mathematical Problems in Engineering (Search for Journal in Brave)

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Cites Work

• The convex geometry of linear inverse problems
• Weighted nuclear norm minimization and its applications to low level vision
• On gridless sparse methods for multi-snapshot direction of arrival estimation
• Computationally efficient maximum likelihood estimation of structured covariance matrices
• SDPT3 — A Matlab software package for semidefinite programming, Version 1.3
• SPICE: A Sparse Covariance-Based Estimation Method for Array Processing
• Sensitivity to Basis Mismatch in Compressed Sensing
• Atomic Norm Denoising With Applications to Line Spectral Estimation
• A Discretization-Free Sparse and Parametric Approach for Linear Array Signal Processing
• Direction of Arrival Estimation Using Co-Prime Arrays: A Super Resolution Viewpoint
• On Gridless Sparse Methods for Line Spectral Estimation From Complete and Incomplete Data
• Enhancing Sparsity and Resolution via Reweighted Atomic Norm Minimization
• Iterative Methods for Subspace and DOA Estimation in Nonuniform Noise
• Compressed Sensing Off the Grid
• Restricted $p$-Isometry Properties of Nonconvex Matrix Recovery
• Towards a Mathematical Theory of Super‐resolution
• Compressed sensing

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