References & Further Reading

References

1. S. Rao, Introduction to mmWave Sensing: FMCW Radars, Texas Instruments, 2017

   TI application note covering IWR/AWR mmWave radar platforms, FMCW principles, and practical system design, discussed in Section 31.1.

2. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, Image Quality Assessment: From Error Visibility to Structural Similarity, 2004

   Defines the SSIM metric and demonstrates the limitations of MSE-based quality measures, discussed in Section 31.4.

3. R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, The Unreasonable Effectiveness of Deep Features as a Perceptual Metric, 2018

   Introduces LPIPS, a learned perceptual metric using deep features, discussed in Section 31.4.

4. A. Wirgin, The Inverse Crime, 2004

   Formalises the inverse crime in computational inverse problems, discussed in Section 31.2.

5. D. Colton and R. Kress, Inverse Acoustic and Electromagnetic Scattering Theory, Springer, 3rd ed., 2013

   Classic text on inverse scattering that covers model mismatch and error bounds, discussed in Sections 31.2 and 31.3.

6. J. Hoydis, S. Cammerer, F. Ait Aoudia, A. Vem, N. Binder, G. Marcus, and A. Keller, Sionna: An Open-Source Library for Next-Generation Physical Layer Research, 2023

   GPU-accelerated wireless simulation with differentiable ray tracing, discussed in Sections 31.2 and 31.3.

7. S. Pinilla, T. Shao, Y. C. Eldar, and H. V. Poor, DeepInverse: A Python Library for Imaging with Deep Learning, 2023

   Open-source library for deep inverse problems supporting PnP, unrolling, diffusion, and self-supervised methods, discussed in Section 31.5.

8. H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, nuScenes: A Multimodal Dataset for Autonomous Driving, 2020

   Large-scale driving dataset with radar, camera, and LiDAR data, discussed in Section 31.2.

9. A. Alkhateeb, DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications, 2019

   Ray-traced massive MIMO channel dataset for both communication and sensing, discussed in Section 31.2.

10. A. F. Molisch, Wireless Communications, Wiley, 3rd ed., 2023

    Comprehensive wireless textbook covering channel measurement and calibration techniques, discussed in Section 31.1.

11. L. Wasserman, All of Statistics: A Concise Course in Statistical Inference, Springer, 2004

    Statistical foundations for Monte Carlo methodology, confidence intervals, and hypothesis testing, discussed in Section 31.3.

12. M. Cheney and B. Borden, Fundamentals of Radar Imaging, SIAM, 2009

    Radar imaging theory covering array geometries and detection metrics, discussed in Sections 31.1 and 31.4.

13. A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, J. Xiao, L. Yi, and F. Yu, ShapeNet: An Information-Rich 3D Model Repository, 2015

    Large-scale 3D shape dataset used for generating synthetic RF imaging targets, discussed in Section 31.2.

14. J. Adler, H. Kohr, and O. Oktem, Operator Discretization Library (ODL), 2017. [Link]

    Python framework for inverse problems with operator abstraction, discussed in Section 31.5.

15. P. Vandewalle, J. Kovacevic, and M. Vetterli, Reproducible Research in Signal Processing, 2009

    Principles of reproducible research providing the framework for the reproducibility discussion in Section 31.5.

16. G. Caire, On the Illumination and Sensing Model for RF Imaging, 2026

    The unifying observation model and Kronecker-structured sensing matrix framework used throughout this book, discussed in Section 31.3.

Further Reading

• TI mmWave radar development

  Texas Instruments, 'mmWave Sensing Estimator and mmWave SDK User's Guide,' TI documentation, 2023

  Comprehensive documentation for developing custom radar applications on TI mmWave platforms, including raw ADC data capture discussed in Section 31.1.

• Electromagnetic simulation

  A. Taflove, A. Oskooi, and S. G. Johnson, 'Advances in FDTD Computational Electrodynamics,' Artech House, 2013

  In-depth treatment of FDTD simulation for electromagnetic problems, complementing the forward model hierarchy of Section 31.3.

• Benchmarking inverse problems

  M. Muckley et al., 'Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction,' IEEE Trans. Medical Imaging, vol. 40, 2021

  Example of a well-designed benchmark for learned imaging (MRI), providing a model for RF imaging benchmarks discussed in Section 31.4.

• Reproducible signal processing research

  P. Vandewalle, J. Kovacevic, and M. Vetterli, 'Reproducible Research in Signal Processing,' IEEE Signal Processing Magazine, vol. 26, no. 3, 2009

  Principles of reproducible research in signal processing, providing the framework for Section 31.5.

• Statistical methods for algorithm comparison

  S. M. Kay, 'Fundamentals of Statistical Signal Processing: Estimation Theory,' Prentice Hall, 1993

  Rigorous treatment of estimation and hypothesis testing relevant to the Monte Carlo methodology of Section 31.3.

• DeepInverse documentation and tutorials

  deepinv.github.io — Official documentation with tutorials covering PnP, unrolled networks, diffusion, and self-supervised methods

  Hands-on guide for implementing the reconstruction methods discussed throughout this book using the DeepInverse library (Section 31.5).