Effectiveness of the tail-atomic norm in gridless spectrum estimation
From MaRDI portal
Publication:6567394
DOI10.1016/J.ACHA.2024.101658MaRDI QIDQ6567394
Publication date: 5 July 2024
Published in: Applied and Computational Harmonic Analysis (Search for Journal in Brave)
Cites Work
- Title not available (Why is that?)
- A mathematical introduction to compressive sensing
- Enhancing sparsity by reweighted \(\ell _{1}\) minimization
- Sparse frame DOA estimations via a rank-one correlation model for low SNR and limited snapshots
- Zur Theorie der quadratischen und bilinearen Formen von unendlichvielen Veränderlichen. I. Teil: Theorie der \(L\)Formen.
- Über den Variabilitätsbereich der Fourierschen Konstanten von positiven harmonischen Funktionen.
- Über den Zusammenhang der Extremen von harmonischen Funktionen mit ihren Koeffizienten und über den Picard-Landauschen Satz.
- Spark-level sparsity and the \(\ell_1\) tail minimization
- The convex geometry of linear inverse problems
- Approximate support recovery of atomic line spectral estimation: a tale of resolution and precision
- Super-resolution, Extremal Functions and the Condition Number of Vandermonde Matrices
- Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
- Atomic Decomposition by Basis Pursuit
- Modified-CS: Modifying Compressive Sensing for Problems With Partially Known Support
- Atomic Norm Denoising With Applications to Line Spectral Estimation
- Enhancing Sparsity and Resolution via Reweighted Atomic Norm Minimization
- Off-the-Grid Line Spectrum Denoising and Estimation With Multiple Measurement Vectors
- Atomic Norm Minimization for Modal Analysis From Random and Compressed Samples
- The Carathéodory–Fejér–Pisarenko Decomposition and Its Multivariable Counterpart
- Compressed Sensing Off the Grid
- Towards a Mathematical Theory of Super‐resolution
- For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution
- Stable signal recovery from incomplete and inaccurate measurements
- Maximum likelihood line spectral estimation in the signal domain: a rank-constrained structured matrix recovery approach
- Multichannel frequency estimation in challenging scenarios via structured matrix embedding and recovery (StruMER)
- Recovery guarantee analyses of joint sparse recovery via tail \(\ell_{2,1}\) minimization
This page was built for publication: Effectiveness of the tail-atomic norm in gridless spectrum estimation
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6567394)
