Compressive sensing for subsurface imaging using ground penetrating radar

DOI10.1016/j.sigpro.2009.03.030zbMath1178.94072OpenAlexW2001969261MaRDI QIDQ1032423

Ali C. Gurbuz, James H. McClellan, Waymond R. jun. Scott

Publication date: 26 October 2009

Published in: Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.sigpro.2009.03.030

zbMATH Keywords

sparsitycompressive sensingsource localizationsynthetic apertureground penetrating radar (GPR)\(\ell _{1}\) minimizationsubsurface imaging

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (5)

Electromagnetic 3D subsurface imaging with source sparsity for a synthetic object ⋮ Sparse microwave imaging: principles and applications ⋮ Compressed sensing SAR imaging based on sparse representation in fractional Fourier domain ⋮ Sparsest representations and approximations of an underdetermined linear system ⋮ Two-dimensional random projection

Uses Software

• PDCO
• CoSaMP

Cites Work

• Unnamed Item
• CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
• A simple proof of the restricted isometry property for random matrices
• Applications of second-order cone programming
• The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
• Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
• Stable recovery of sparse overcomplete representations in the presence of noise
• Signal Reconstruction From Noisy Random Projections
• Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
• Compressed Sensing and Redundant Dictionaries
• Superresolution via Sparsity Constraints
• Atomic Decomposition by Basis Pursuit
• A sparse signal reconstruction perspective for source localization with sensor arrays
• Stable signal recovery from incomplete and inaccurate measurements
• Pre-stack migration applied to GPR for landmine detection
• Compressed sensing

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