Imaging With WiFi

Vakalis, Stavros; Gong, Liang; Nanzer, Jeffrey A.

doi:10.1109/access.2019.2902315

Cited by 40 publications

(11 citation statements)

References 35 publications

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“…These techniques overcome the drawbacks of traditional optical imaging (e.g., vulnerability to illumination conditions and occlusions). In [7], Vakalis et al broadly classified all existing RFbased imaging systems into three categories-mechanical and electronic scanning imagers [20], holographic imaging systems [21], and staring-type imagers [22], [23]. However, a common drawback of these systems is that they cannot achieve cost-effective, real-time imaging.…”

Section: B Imaging Using Rf Signalsmentioning

confidence: 99%

“…However, a common drawback of these systems is that they cannot achieve cost-effective, real-time imaging. Therefore, Vakalis et al developed a new microwave computational imaging system that only requires a low receiver gain and does not rely on mechanical or electrical beam scanning [7]. Nevertheless, the construction of such an imaging system is timeconsuming, and the imaging performance requires further improvements.…”

Section: B Imaging Using Rf Signalsmentioning

confidence: 99%

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RF-Inpainter: Multimodal Image Inpainting Based on Vision and Radio Signals

et al. 2022

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This study demonstrates the feasibility of image inpainting using both visual information and radio frequency (RF) signals. Recent developments in imaging and vision-based technologies using RF signals have revealed the potential of leveraging multimodal information to enhance image inpainting performance. In this context, we propose RF-Inpainter-a novel inpainting method that integrates visual and wireless information by fusing defective RGB images with the received signal strength indicator (RSSI) using a deep auto-encoder model. The inpainting performance of RF-Inpainter is evaluated using experimentally obtained images and RSSI datasets in an indoor environment. Image-only inpainting and RSSI-only inpainting models are used as baselines to illustrate the superiority of RF-Inpainter over inpainting methods based on a single modality. The results establish that RF-Inpainter generates satisfactory inpainted images in most experimental scenarios, achieving a maximum improvement of 36.4% and 14.6% in terms of mean peak signal-to-noise ratio (PSNR) and mean structural similarity index (SSIM), respectively.

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Section: B Imaging Using Rf Signalsmentioning

confidence: 99%

Section: B Imaging Using Rf Signalsmentioning

confidence: 99%

RF-Inpainter: Multimodal Image Inpainting Based on Vision and Radio Signals

et al. 2022

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“…The k-space components constituting the signature of a point target, for example, are determined by the angular extent of the aperture and the relationship between the aperture signal components according to Eq. ( 11) or (14). For a given aperture, the components of the k vector corresponding to each aperture position can be computed simply by projecting the vector connecting the aperture element to the target position onto the Cartesian axes, as in [43].…”

Section: K-space Representation and Processingmentioning

confidence: 99%

“…In both cases, interferometric processing enables image estimation of the source or scattering structures through signal correlations over an array. As in the case of active systems, techniques pioneered in large-scale radio frequency systems have since been distilled into deployable systems for passive imaging of noise sources [9], [10], [11], [12] and imaging with ambient wifi signals [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Unified Reciprocal Space Processing for Short-Range Active and Passive Imaging Systems

Diebold

Fromentèze

Kpré³

et al. 2022

IEEE Open J. Antennas Propag.

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Large scale sensing systems increasingly operate in short-range geometries, providing three-dimensional imaging capabilities that leverage the large number of degrees of freedom of distributed arrays. The two most powerful modes of operation include active, multiple-input multiple-output arrays, in which an image is processed from signals measured using different pairs of transmitting and receiving antennas; and passive, synthetic aperture interferometric radiometer arrays, which can image using ambient, non-cooperative radiation by processing signals measured at different receiving antenna pairs. In this paper, we explore the analogy between these two imaging modes, and outline a generalized framework for analyzing these systems in the array near field. Focusing on the similarities between the formalisms describing the operation of generic active and passive systems, it is proposed, to our knowledge for the first time in the scientific literature, to adapt a Fourier-based imaging technique optimized for short-range active MIMO systems to passive technologies, interrogating the emissivity of a spatially distributed target. The systematic method presented in this paper provides a framework for characterizing and optimizing short range coherent or incoherent systems used in threat detection and non-destructive testing applications.

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“…However, this technique requires measurements to be captured in a phase-coherent fashion using two antennas, one is fixed as a reference antenna and another is moved on a 2D plane for scanning. These prior works use 2.4GHz/5GHz WiFi devices, which can only extend the effective aperture by SAR to break their limitation of antenna aperture [30]- [33]. This leads to the fact that imaging quality is vulnerable under the moving trajectory tracking error and makes it impossible to achieve real-time imaging.…”

Section: Introductionmentioning

confidence: 99%

High-Resolution WiFi Imaging with Reconfigurable Intelligent Surfaces

He¹,

Zhang²,

Chen³

2021

Preprint

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WiFi-based imaging enables pervasive sensing in a privacy-preserving and cost-effective way. However, most of existing methods either require specialized hardware modification or suffer from poor imaging performance due to the fundamental limit of off-the-shelf commodity WiFi devices in spatial resolution. We observe that the recently developed reconfigurable intelligent surface (RIS) could be a promising solution to overcome these challenges. Thus, in this paper, we propose a RIS-aided WiFi imaging framework to achieve high-resolution imaging with the off-the-shelf WiFi devices. Specifically, we first design a beamforming method to achieve the first-stage imaging by separating the signals from different spatial locations with the aid of the RIS. Then, we propose an optimization-based super-resolution imaging algorithm by leveraging the low rank nature of the reconstructed object. During the optimization, we also explicitly take into account the effect of finite phase quantization in RIS to avoid the resolution degradation due to quantization errors. Simulation results demonstrate that our framework achieves median root mean square error (RMSE) of 0.03 and median structural similarity (SSIM) of 0.52. The visual results show that high-resolution imaging results are achieved with simulation signals at 5 GHz that are matched with commercial WiFi 802.11n/ac protocols.

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Imaging With WiFi

Cited by 40 publications

References 35 publications

RF-Inpainter: Multimodal Image Inpainting Based on Vision and Radio Signals

RF-Inpainter: Multimodal Image Inpainting Based on Vision and Radio Signals

Unified Reciprocal Space Processing for Short-Range Active and Passive Imaging Systems

High-Resolution WiFi Imaging with Reconfigurable Intelligent Surfaces

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