References & Further Reading

References

J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel, Domain randomization for transferring deep neural networks from simulation to the real world, 2017
Foundational domain randomisation paper for sim-to-real transfer, directly applicable to RF imaging domain adaptation strategies in Section 32.1.
R. Bommasani, D. A. Hudson, E. Adeli, et al., On the opportunities and risks of foundation models, 2021
Comprehensive survey defining the foundation model paradigm discussed in Section 32.4.
A. N. Angelopoulos, S. Bates, E. J. Candes, M. I. Jordan, and L. Lei, Conformal prediction: A gentle introduction, 2023
Introduction to conformal prediction for distribution-free uncertainty quantification, relevant to Section 32.6.
M. Tancik, V. Casser, X. Yan, S. Pradhan, B. Mildenhall, P. P. Srinivasan, J. T. Barron, and H. Kretzschmar, Block-NeRF: Scalable large scene neural view synthesis, 2022
Scalable neural scene representation via spatial partitioning, providing the hierarchical framework discussed in Section 32.5.
M. Genzel, J. Macdonald, and M. Marz, Solving inverse problems with deep neural networks: Robustness included?, 2022
Analysis of stability and robustness of neural network reconstructions, relevant to Sections 32.1 and 32.6.
S. Tulsiani, H. Su, L. J. Guibas, A. A. Efros, and J. Malik, Learning shape abstractions by assembling volumetric primitives, 2017
Predicting geometric primitives from visual data, inspiring the primitive-based RF scene decomposition of Section 32.3.
A. Pumarola, E. Corona, G. Pons-Moll, and F. Moreno-Noguer, D-NeRF: Neural radiance fields for dynamic scenes, 2021
Dynamic NeRF framework for time-varying scenes, providing the template for 4D RF imaging discussed in Section 32.2.
F. Liu, C. Masouros, A. P. Petropulu, H. Griffiths, and L. Hanzo, Joint radar and communication design: Applications, state-of-the-art, and the road ahead, 2022
Survey of joint radar-communication design, providing context for the imaging capacity discussion in Section 32.6.
E. J. Candes and C. Fernandez-Granda, Towards a mathematical theory of super-resolution, 2014
Foundational theory of super-resolution via atomic norm minimisation, relevant to the resolution limits discussion in Section 32.6.
V. Monga, Y. Li, and Y. C. Eldar, Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing, 2021
Survey of algorithm unrolling methods, providing evaluation methodology for Section 32.7.
G. Ongie, A. Jalal, C. A. Metzler, R. G. Baraniuk, A. G. Dimakis, and R. Willett, Deep learning techniques for inverse problems in imaging, 2020
Comprehensive survey establishing evaluation standards for learned imaging methods discussed in Section 32.7.
J. Pineau, P. Vincent, K. Gaber, S. Bengio, et al., Improving reproducibility in machine learning research, 2021
Reproducibility guidelines applicable to ML-based RF imaging research, informing Section 32.7.
L. C. Potter, E. Ertin, J. T. Parker, and M. Cetin, Sparsity and compressed sensing in radar imaging, 2010
Foundational paper on CS for SAR imaging, exemplifying rigorous methodology in Section 32.7.
Z. C. Lipton and J. Steinhardt, Troubling trends in machine learning scholarship, 2018
Critical analysis of problematic practices in ML papers, applicable to RF imaging writing standards in Section 32.7.
G. Caire, On the illumination and sensing model for RF imaging, 2026
Caire's unified framework connecting diffraction tomography and MIMO radar models. The forward model $\mathbf{y} = \mathbf{A}\boldsymbol{\gamma} + \mathbf{w}$ is the foundation for all open problems in this chapter.