References & Further Reading

References

G. Caire, A. Rezaei, W. Zhang, On the Illumination and Sensing Model for RF Imaging, 2024
The primary reference for this chapter. Derives the unified forward model $\ntn{img_model} = \ntn{sens}\ntn{refl} + \ntn{noise}$ from both the diffraction-tomography and radar/wireless perspectives, and establishes the Kronecker structure exploited throughout.
G. H. Golub and C. F. Van Loan, Matrix Computations, Johns Hopkins University Press, 4th edition ed., 2013
The definitive reference for Kronecker product algebra (Chapter 4), SVD theory (Chapter 2), and preconditioning for iterative methods (Chapter 11). Essential background for Sections 7.1--7.4.
C. F. Van Loan and N. Pitsianis, Approximation with Kronecker Products, Kluwer Academic, 1993
Establishes the theory of nearest Kronecker product (NKP) approximation, showing that structured matrices can be optimally approximated by Kronecker products. Foundation for the approximate Kronecker decomposition in Section 7.1.
T. G. Kolda and B. W. Bader, Tensor Decompositions and Applications, 2009
Comprehensive survey of tensor algebra including mode-$k$ products, Tucker decomposition, and computational complexity. The tensor perspective on Kronecker structure in Section 7.1.
J. Li and P. Stoica, MIMO Radar Signal Processing, Wiley-IEEE Press, 2009
Standard reference for MIMO radar virtual aperture concepts. Chapters 5--6 develop the Kronecker structure of the MIMO steering matrix and its spectral properties (Section 7.2).
S. Jokar and V. Mehrmann, Sparse Solutions to Underdetermined Kronecker Product Systems, 2009
Proves the RIP inheritance theorem for Kronecker products (Theorem 7.3.3). Shows that the RIP constant degrades additively while the sparsity level multiplies across factors.
S. Foucart and H. Rauhut, A Mathematical Introduction to Compressive Sensing, Springer, 2013
Comprehensive treatment of compressed sensing theory: RIP, coherence bounds, and recovery guarantees. Chapters 5--9 provide the theoretical foundation for Section 7.3.
H. W. Engl, M. Hanke, and A. Neubauer, Regularization of Inverse Problems, Kluwer Academic, 1996
Standard reference for ill-posed problems. Spectral regularization theory (Section 7.2) and condition number analysis for inverse problems.
R. G. Baraniuk and P. Steeghs, Compressive Radar Imaging, 2007
Pioneering work applying compressed sensing to radar imaging. Demonstrates that structured radar sensing matrices can support sparse recovery despite not satisfying standard RIP conditions.
M. Lustig, D. L. Donoho, and J. M. Pauly, Sparse MRI: The Application of Compressed Sensing for Rapid MR Imaging, 2007
Foundational paper on compressed sensing for MRI. The partial Fourier sensing model is structurally analogous to our Kronecker-structured RF sensing operator.
L. Manzoni, A. Rezaei, and G. Caire, Wavefield Networked Sensing for Multi-Static RF Imaging, 2024
Develops the k-space tessellation framework referenced in Section 7.2 (PSF analysis) and the multi-view fusion approach exploiting per-pair Kronecker structure (Section 7.5).