An Overview of Advances in Signal Processing Techniques for Classical and Quantum Wideband Synthetic Apertures

Vouras, Peter; Mishra, Kumar Vijay; Artusio‐Glimpse, Alexandra B.; Pinilla, Samuel; Xenaki, Angeliki; Griffith, David Wark; Егиазарян, Карен

doi:10.48550/arxiv.2205.05602

Cited by 2 publications

(2 citation statements)

References 240 publications

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“…SA plays an essential role in CSI since it directly affects the structure of the measurements, similar to phase imaging or computer tomography. [2]. Consequently, the quality of the estimated spectral image primarily depends on the sensing procedure and the recovery method.…”

Section: Introductionmentioning

confidence: 99%

D$^\text{2}$UF: Deep Coded Aperture Design and Unrolling Algorithm for Compressive Spectral Image Fusion

Jácome¹,

Bacca²,

Argüello³

2022

Preprint

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Compressive spectral imaging (CSI) has attracted significant attention since it employs synthetic apertures to codify spatial and spectral information, sensing only 2D projections of the 3D spectral image. However, these optical architectures suffer from a trade-off between the spatial and spectral resolution of the reconstructed image due to technology limitations. To overcome this issue, compressive spectral image fusion (CSIF) employs the projected measurements of two CSI architectures with different resolutions to estimate a high-spatial high-spectral resolution. This work presents the fusion of the compressive measurements of a low-spatial high-spectral resolution coded aperture snapshot spectral imager (CASSI) architecture and a high-spatial low-spectral resolution multispectral color filter array (MCFA) system. Unlike previous CSIF works, this paper proposes joint optimization of the sensing architectures and a reconstruction network in an end-to-end (E2E) manner. The trainable optical parameters are the coded aperture (CA) in the CASSI and the colored coded aperture in the MCFA system, employing a sigmoid activation function and regularization function to encourage binary values on the trainable variables for an implementation purpose. Additionally, an unrolling-based network inspired by the alternating direction method of multipliers (ADMM) optimization is formulated to address the reconstruction step and the acquisition systems design jointly. Finally, a spatial-spectral inspired loss function is employed at the end of each unrolling layer to increase the convergence of the unrolling network. The proposed method outperforms previous CSIF methods, and experimental results validate the method with real measurements.

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Section: Introductionmentioning

confidence: 99%

D$^\text{2}$UF: Deep Coded Aperture Design and Unrolling Algorithm for Compressive Spectral Image Fusion

Jácome¹,

Bacca²,

Argüello³

2022

Preprint

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“…In mobile radio [11] and vehicular networks [12], the channel is highly dynamic and its prior estimates may be outdated. Therefore, a general spectral coexistence scenario comprises of a common receiver [13], wherein both radar and communications channels and their respective transmit signals are unknown.…”

Section: Introductionmentioning

confidence: 99%

Multi-Dimensional Dual-Blind Deconvolution Approach Toward Joint Radar-Communications

Jácome

Mishra

Vargas

et al. 2022

2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC)

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We consider a joint multiple-antenna radar-communications system in a co-existence scenario. Contrary to conventional applications, wherein at least the radar waveform and communications channel are known or estimated a priori, we investigate the case when the channels and transmit signals of both systems are unknown. In radar applications, this problem arises in multistatic or passive systems, where transmit signal is not known. Similarly, highly dynamic vehicular or mobile communications may render prior estimates of wireless channel unhelpful. In particular, the radar signal reflected-off multiple targets is overlaid with the multi-carrier communications signal. In order to extract the unknown continuous-valued target parameters (range, Doppler velocity, and direction-of-arrival) and communications messages, we formulate the problem as a sparse dual-blind deconvolution and solve it using atomic norm minimization. Numerical experiments validate our proposed approach and show that precise estimation of continuous-valued channel parameters, radar waveform, and communications messages is possible up to scaling ambiguities.

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An Overview of Advances in Signal Processing Techniques for Classical and Quantum Wideband Synthetic Apertures

Cited by 2 publications

References 240 publications

D$^\text{2}$UF: Deep Coded Aperture Design and Unrolling Algorithm for Compressive Spectral Image Fusion

D$^\text{2}$UF: Deep Coded Aperture Design and Unrolling Algorithm for Compressive Spectral Image Fusion

Multi-Dimensional Dual-Blind Deconvolution Approach Toward Joint Radar-Communications

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