References & Further Reading

References

R. Tibshirani, Regression Shrinkage and Selection via the Lasso, 1996
The original LASSO paper. Section s01 applies the LASSO to the RF imaging sensing matrix and discusses parameter selection strategies.
S. S. Chen, D. L. Donoho, and M. A. Saunders, Atomic Decomposition by Basis Pursuit, 2001
The Basis Pursuit formulation. Section s01 discusses BP as the gold standard for noiseless recovery but notes its $O(N^3)$ cost for interior-point methods.
A. Beck and M. Teboulle, A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems, 2009
The FISTA paper introducing Nesterov acceleration for proximal gradient, achieving $O(1/t^2)$ convergence. Applied throughout the chapter for LASSO and group LASSO.
S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers, 2011
The comprehensive ADMM tutorial. Section s03 follows Boyd et al. for TV-regularized imaging with adaptive penalty parameter.
J. A. Tropp and A. C. Gilbert, Signal Recovery from Random Measurements via Orthogonal Matching Pursuit, 2007
Recovery guarantees for OMP under the RIP and coherence conditions. Section s04 presents OMP for RF imaging.
D. Needell and J. A. Tropp, CoSaMP: Iterative Signal Recovery from Incomplete and Inaccurate Samples, 2009
CoSaMP with RIP-based guarantees comparable to BP. Section s04 presents CoSaMP as a robust greedy alternative with self-correction capability.
T. Blumensath and M. E. Davies, Iterative Hard Thresholding for Compressed Sensing, 2009
IHT with RIP-based convergence guarantees. Section s04 presents IHT as a middle ground between greedy and convex.
L. I. Rudin, S. Osher, and E. Fatemi, Nonlinear Total Variation Based Noise Removal Algorithms, 1992
The foundational TV denoising paper (ROF model). Section s03 extends TV to inverse problems in RF imaging.
K. Bredies, K. Kunisch, and T. Pock, Total Generalized Variation, 2010
TGV definition and properties. Section s03 discusses TGV for piecewise-smooth RF scenes.
G. Tang, B. N. Bhaskar, P. Shah, and B. Recht, Compressed Sensing Off the Grid, 2013
Establishes the atomic norm framework for gridless sparse recovery via SDP. Section s05 applies this to RF imaging.
M. Cetin and W. C. Karl, Feature-Enhanced Synthetic Aperture Radar Image Formation Based on Nonquadratic Regularization, 2001
Pioneering application of sparsity-promoting regularization to SAR imaging. Motivates the imaging applications throughout this chapter.
Y. C. Eldar and M. Mishali, Block Sparsity and Sampling Over a Union of Subspaces, 2009
Block sparsity framework and group LASSO recovery guarantees. Section s02 applies this to multi-measurement RF imaging.
D. Malioutov, M. Cetin, and A. S. Willsky, A Sparse Signal Reconstruction Perspective for Source Localization with Sensor Arrays, 2005
Seminal paper connecting array processing to sparse recovery. Sections s01 and s05 draw on this connection.
M. Pesavento, D. Ciuonzo, and A. M. Zoubir, Compact Formulations for Group Sparse Recovery in Sensor Array Processing, 2023
Pesavento compact formulation for iteratively reweighted group LASSO. Section s02 presents this for multi-frequency RF imaging.
Y. Chi, L. L. Scharf, and A. Pezeshki, Sensitivity to Basis Mismatch in Compressed Sensing, 2011
Analysis of basis mismatch effects on sparse recovery. Section s05 discusses the mismatch problem motivating gridless methods.
C. M. Stein, Estimation of the Mean of a Multivariate Normal Distribution, 1981
SURE framework for unbiased risk estimation. Section s01 applies SURE for regularization parameter selection.