Computational polarimetric microwave imaging

Fromentèze, Thomas; Yurduseven, Okan; Boyarsky, Michael; Gollub, Jonah N.; Marks, Daniel L.; Smith, David R.

doi:10.1364/oe.25.027488

Cited by 58 publications

(81 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In an experimental application and under unfavorable body postures, it may be possible to observe artifacts caused by these multiple bounces, particularly between the legs and under the arms of the mannequin. However, the use of polarimetric information in such a context [17] could help to identify and filter these detrimental effects [29].…”

Section: B Numerical Validationmentioning

confidence: 99%

A Transverse Spectrum Deconvolution Technique for MIMO Short-Range Fourier Imaging

Fromentèze

Yurduseven

Berland

et al. 2019

IEEE Trans. Geosci. Remote Sensing

Self Cite

View full text Add to dashboard Cite

The growing need for high-performance imaging tools for terrorist threat detection and medical diagnosis has led to the development of new active architectures in the microwave and millimeter range. Notably, multiple-input multiple-output systems can meet the resolution constraints imposed by these applications by creating large, synthetic radiating apertures with a limited number of antennas used independently in transmitting and receiving signals. However, the implementation of such systems is coupled with strong constraints in the software layer, requiring the development of reconstruction techniques capable of interrogating the observed scene by optimizing both the resolution of images reconstructed in two or three dimensions and the associated computation times. In this paper, we first review the formalisms and constraints associated with each application by taking stock of efficient processing techniques based on spectral decompositions, and then, we present a new technique called the transverse spectrum deconvolution range migration algorithm allowing us to carry out reconstructions that are both faster and more accurate than with conventional Fourier domain processing techniques. This paper is particularly relevant to the development of new computational imaging tools that require, even more pronouncedly than in the case of conventional architectures, fast image computing techniques despite a very large number of radiating elements interrogating the scene to be imaged.Index Terms-MIMO radar, microwave imaging, millimeter wave radar, multistatic radar.

show abstract

Section: B Numerical Validationmentioning

confidence: 99%

A Transverse Spectrum Deconvolution Technique for MIMO Short-Range Fourier Imaging

Fromentèze

Yurduseven

Berland

et al. 2019

IEEE Trans. Geosci. Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…The syntheses of random waves in UI partly trace back to time-reversal [45] and computational microwave imaging [46]- [50]. The latter trades the hardware complexity and costs, which are raised by transceiver arrays or mechanical scans, for the computational costs of the image recovery.…”

Section: A Related Workmentioning

confidence: 99%

“…Highly dispersive customized apertures, e.g. complex metamaterials [47], [49], [50] or leaky reverberant cavities [46], [48], form virtual transceiver arrays. These expand excitations at sufficiently different frequencies into distinct spatial codes, i.e.…”

Section: A Related Workmentioning

confidence: 99%

Notice of Removal: Random incident sound waves for fast compressed pulse-echo ultrasound imaging

Schiffner

Schmitz

2017

2017 IEEE International Ultrasonics Symposium (IUS)

View full text Add to dashboard Cite

Established image recovery methods in fast ultrasound imaging, e.g. delay-and-sum, trade the image quality for the high frame rate. Cutting-edge inverse scattering methods based on compressed sensing (CS) disrupt this tradeoff via a priori information. They iteratively recover a high-quality image from only a few sequential pulse-echo measurements or less echo signals, if (i) a known dictionary of structural building blocks represents the image almost sparsely, and (ii) their individual pulse echoes, which are predicted by a linear model, are sufficiently uncorrelated. The exclusive modeling of the incident waves as steered plane waves or cylindrical waves, however, has so far limited the convergence speed, the image quality, and the potential to meet condition (ii).Motivated by the benefits of randomness in CS, a novel method for the fast compressed acquisition and the subsequent recovery of images is proposed to overcome these limitations. It recovers the spatial compressibility fluctuations in weakly-scattering soft tissue structures, where an orthonormal basis meets condition (i), by a sparsity-promoting ℓq-minimization method, q ∈ [0; 1]. A realistic d-dimensional model, d ∈ {2, 3}, accounting for diffraction, single monopole scattering, the combination of powerlaw absorption and dispersion, and the specifications of a planar transducer array, predicts the pulse echoes of the individual basis functions. Three innovative types of incident waves, whose syntheses leverage random apodization weights, time delays, or combinations thereof, aid in meeting condition (ii).In two-dimensional numerical simulations, single realizations of these waves outperform the prevalent quasi-plane wave for both the canonical and the Fourier bases. They significantly decorrelate the pulse echoes, e.g. they reduce the full extents at half maximum of the point spread functions by up to 73.7 %. For a tissue-mimicking phantom and q ∈ {1, 0.5}, they improve the convergence speed and the image quality in terms of the mean structural similarity indices and the relative root meansquared errors by up to {2.7 %, 22.9 %}, {44.1 %, 55.5 %}, and {42 %, 60.5 %}, respectively.Index Terms-random incident waves, inverse scattering, compressed sensing, sparse regularization, ultrafast ultrasound imaging, computational ultrasound imaging, fast multipole method, GPU computing

show abstract

“…In this framework, what is required is a reliable, low-cost and rapid method for the imaging of metallic and non-metallic (dielectric) concealed objects. Solving the inverse problem is a computationally demanding task and is conventionally done by linearizing the forwardmodel and solving it to retrieve a qualitative estimate of the object function, such as the susceptibility distribution [28][29][30][31][32][33][34]. The linear inverse scattering algorithms, which are conventionally based upon a Born approximation, have the advantage of requiring a small amount of computational time due to their linear approach.…”

Section: Introductionmentioning

confidence: 99%

Indirect microwave holography and Through-Wall imaging

Yurduseven¹,

Elsdon²

2019

Frelsi

Self Cite

View full text Add to dashboard Cite

In this paper, a review of indirect microwave holography for through-wall imaging is presented. Indirect microwave holography is an imaging technique, enabling the complex object scattered fields (amplitude and phase) to be mathematically recovered from intensity-only, scalar microwave measurements. By removing the requirement to use vector measurement equipment to directly measure the complex fields, indirect microwave holography significantly reduces the cost of the imaging system and simplifies the hardware implementation. The application of a back-propagation algorithm enables the reconstructed amplitude and phase images to be obtained at the plane of the concealed object. In order to demonstrate the validity of the reviewed approach, experimental work is carried out on a metallic gun concealed under a 5 cm thick plywood wall and it is demonstrated that the indirect microwave holographic TWI can produce good resolution amplitude and phase images when back-propagation is applied. TWI of a concealed dielectric box representing non-metallic ordnance is also performed to demonstrate the ability of the technique to reconstruct through-wall images of concealed dielectric objects. An investigation of the resolution characteristics of the system suggests diffraction limited resolution can be achieved.

show abstract

Computational polarimetric microwave imaging

Cited by 58 publications

References 57 publications

A Transverse Spectrum Deconvolution Technique for MIMO Short-Range Fourier Imaging

A Transverse Spectrum Deconvolution Technique for MIMO Short-Range Fourier Imaging

Notice of Removal: Random incident sound waves for fast compressed pulse-echo ultrasound imaging

Indirect microwave holography and Through-Wall imaging

Contact Info

Product

Resources

About