W-band sparse synthetic aperture for computational imaging

Venkatesh, Suresh; Viswanathan, Naren; Schurig, David

doi:10.1364/oe.24.008317

Cited by 15 publications

(11 citation statements)

References 20 publications

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“…However, it should be noted that there is trade-off between the orthogonality and signal-to-noise ratio of the physical measurements. [82] Using Shannon-Hartley theorem and standard error propagation, one can relate the singular values of H matrix and measurement noise 𝛿g to determine the total measurement-added information as follows…”

Section: Reconfigurable Field Of View and 3d Imaging With Curved Orig...mentioning

confidence: 99%

Origami Microwave Imaging Array: Metasurface Tiles on a Shape‐Morphing Surface for Reconfigurable Computational Imaging

Venkatesh

Sturm

et al. 2022

Advanced Science

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Origami is the art of paper folding that allows a single flat piece of paper to assume different 3D shapes depending on the fold patterns and the sequence of folding. Using the principles of origami along with computation imaging technique the authors demonstrate a versatile shape‐morphing microwave imaging array with reconfigurable field‐of‐view and scene‐adaptive imaging capability. Microwave/millimeter‐wave based array imaging systems are expected to be the workhorse for sensory perception of future autonomous intelligent systems. The imaging capability of a planar array‐based systems operating in complex scattering conditions have limited field‐of‐view and lack the ability to adaptively reconfigure resolution. To overcome this, here, deviations from planarity and isometry are allowed, and a shape‐morphing computational imaging system is demonstrated. Implemented on a reconfigurable Waterbomb origami surface with 22 active metasurface panels that radiate near‐orthogonal modes across 17–27 GHz, capability to image complex 3D objects in full details minimizing the effects of specular reflections in diffraction‐limited sparse imaging with scene adaptability, reconfigurable cross‐range resolution, and field‐of‐view is demonstrated. Such electromagnetic origami surfaces, through simultaneous surface shape‐morphing ability (potentially with shape‐shifting electronic materials) and electromagnetic field programmability, opens up new avenues for intelligent and robust sensing and imaging systems for a wide range of applications.

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Section: Reconfigurable Field Of View and 3d Imaging With Curved Orig...mentioning

confidence: 99%

Origami Microwave Imaging Array: Metasurface Tiles on a Shape‐Morphing Surface for Reconfigurable Computational Imaging

Venkatesh

Sturm

et al. 2022

Advanced Science

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“…Solving Equation (1) to recover an estimate of the scene information, f est , is an inverse problem. For the CI technique employed in this work, we can leverage several algorithms to this end, such as matched-filtering [45] and least-squares techniques [51,52]. The matched-filter technique is advantageous in that it is a singleshot reconstruction algorithm, and hence the computational load of this algorithm can be minimal.…”

Section: Computational Imaging Using Coded Aperturesmentioning

confidence: 99%

Hardware Enabled Acceleration of Near-Field Coded Aperture Radar Physical Model for Millimetre-Wave Computational Imaging

Sharma¹,

Yurduseven²,

Deka³

et al. 2021

PIER B

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There is an increasing demand in real-time imagery applications such as rapid response to disaster rescue and security screening to name a few. The throughput of a radar imaging system is mainly controlled by two parameters; data acquisition time and signal processing time. To minimize the data acquisition time, various methods are being tried and tested by researchers worldwide. Among them is the computational imaging (CI) technique, which relies on using coded apertures to encode the radar back-scattered measurements onto a set of spatio-temporally incoherent radiation patterns. Such a CI-based imaging approach eliminates the requirement for a raster scan and can substantially simplify the physical hardware architecture. Equally important is the processing time needed to retrieve the scene information from the coded back-scattered measurements. In CI, the simplification in the hardware layer comes at the cost of increased complexity in the signal processing layer due to the indirect mapping and compression of the scene information through the spatio-temporally incoherent transfer function of the coded apertures. To address this particular challenge, this paper presents a hardware-based solution for CI signal processing using a Field Programmable Gate Array (an Xilinx Virtex-7 (XC7VX485T) FPGA chip) architecture. In particular, the proposed method consists of calculating the CI sensing matrix using the FPGA chip and storing it on the FPGA platform for image reconstruction. For the adjoint operation, the calculated sensing matrix is applied on the measured back-scattered waves from the target object. We demonstrate that the FPGA based calculation can reach 21.9 times faster speed than conventional brute-force solutions.

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“…rr r r r r rr (5) In (4) and 5, J(r) denotes the surface current distribution of the frequency-diverse antenna while r and ' r denote the radar and scene coordinates, respectively. The frequencydiverse radar that we study in this paper consists of a single antenna sharing the same aperture to transmit and receive, and hence JTx=JRx and ETx= ERx.…”

Section: Frequency-diverse Computational Imagingmentioning

confidence: 99%

Frequency-Diverse Computational Automotive Radar Technique for Debris Detection

et al. 2020

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Frequency-diversity is a computational imaging technique that can offer all-electronic imaging systems by leveraging spatio-temporally incoherent radiation patterns as an enabling technology. This approach exhibits a significant contrast to conventional imaging modalities, such as synthetic aperture radars (SAR) and phased arrays in that the raster scanning requirement of a scene to be imaged (mechanical or electronic) can be broken and replaced by a quasi-random interrogation of the scene. This aspect of frequency-diverse computational imaging systems significantly simplifies the physical hardware requirements of conventional radars. Despite this advantage, the application of the frequency-diversity technique has been mostly limited to static imaging scenarios, where the position of the scene to be imaged remains fixed over the data acquisition cycle. This limitation hinders the frequencydiverse computational radars from being deployed for applications where the scene dynamics may vary over the data acquisition cycle, such as in automotive radars. In this paper, we demonstrate that by modifying the sensing matrix to account for the movement of the radar platform, frequency-diverse computational imaging radars can be successfully used in debris detection on roads. We show that operating within the frequency band of 77-81 GHz, the presented dynamic frequency-diverse radar technique can produce high fidelity point spread function (PSF) patterns eliminating the distortions caused by the motion of the radar. We also prove that the PSF patterns of the radar are in excellent agreement with theoretical diffraction limited resolution limits.

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W-band sparse synthetic aperture for computational imaging

Cited by 15 publications

References 20 publications

Origami Microwave Imaging Array: Metasurface Tiles on a Shape‐Morphing Surface for Reconfigurable Computational Imaging

Origami Microwave Imaging Array: Metasurface Tiles on a Shape‐Morphing Surface for Reconfigurable Computational Imaging

Hardware Enabled Acceleration of Near-Field Coded Aperture Radar Physical Model for Millimetre-Wave Computational Imaging

Frequency-Diverse Computational Automotive Radar Technique for Debris Detection

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