All-Directions Through-the-Wall Imaging Using a Small Number of Moving Omnidirectional Bi-Static FMCW Transceivers

Yektakhah, Behzad; Sarabandi, Kamal

doi:10.1109/tgrs.2018.2875695

Cited by 28 publications

(13 citation statements)

References 20 publications

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“…On this basis, the generated image and the optical image can be integrated to perform FSBDU as [97] does. Reference [99] proposed a dual static FMCW radar system, which was constructed using a simple wireless synchronization scheme and a broadband omnidirectional antenna. It adopts imaging technology compatible with the FMCW imaging system to provide high-resolution images to detect objects inside a wall, which fully demonstrates the superiority of MMW-Radar in penetration.…”

Section: A Fusion Strategy Based On Discernible Unitsmentioning

confidence: 99%

Multi-Sensor Fusion in Automated Driving: A Survey

2020

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With the significant development of practicability in deep learning and the ultra-highspeed information transmission rate of 5G communication technology will overcome the barrier of data transmission on the Internet of Vehicles, automated driving is becoming a pivotal technology affecting the future industry. Sensors are the key to the perception of the outside world in the automated driving system and whose cooperation performance directly determines the safety of automated driving vehicles. In this survey, we mainly discuss the different strategies of multi-sensor fusion in automated driving in recent years. The performance of conventional sensors and the necessity of multi-sensor fusion are analyzed, including radar, LiDAR, camera, ultrasonic, GPS, IMU, and V2X. According to the differences in the latest studies, we divide the fusion strategies into four categories and point out some shortcomings. Sensor fusion is mainly applied for multi-target tracking and environment reconstruction. We discuss the method of establishing a motion model and data association in multi-target tracking. At the end of the paper, we analyzed the deficiencies in the current studies and put forward some suggestions for further improvement in the future. Through this investigation, we hope to analyze the current situation of multi-sensor fusion in the automated driving process and provide more efficient and reliable fusion strategies.

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Section: A Fusion Strategy Based On Discernible Unitsmentioning

confidence: 99%

Multi-Sensor Fusion in Automated Driving: A Survey

2020

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“…More information about the Rytov model can be found in [24], [27]. The RSS can be obtained by (4) and that is…”

Section: Through-the-wall Moblie Rtimentioning

confidence: 99%

“…In the fields of TTWRI, the main purpose of building layout reconstruction aims at obtaining the overall layout information by observation outside the building, which is very important before entering inside the building. The problem of building layout reconstruction has led to more and more attention in recent years [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Through-the-Wall Image Reconstruction via Reweighted Total Variation and Prior Information in Radio Tomographic Imaging

Guo

Liang

et al. 2020

IEEE Access

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This paper focuses on the application of radio tomographic imaging in through-the-wall image reconstruction. By using the simple wireless communication devices, e.g., Wi-Fi cards, the whole building layout can be reconstructed with only the received signal strength (RSS). For the reconstruction algorithms, the total variation (TV) minimization algorithm has been used to reconstruct the image. However, some false artifacts may exist in the image results due to the inherent defects of the algorithm. The artifacts can be misestimated as wall structures. In this paper, a reweighted total variation and prior information regularization algorithm named as RTV-PIR is proposed to reconstruct the image. This algorithm is based on the TV minimization of the image. And the prior information that the wall is only oriented horizontally or vertically is also considered simultaneously, which is used to keep the wall orientation. To verify the performance of the proposed algorithm, the simulations and experiments based on real data are conducted. It is shown that the RTV-PIR algorithm can get a better result with respect to the root mean square error (RMSE) and the structural similarity (SSIM), in comparison with the state-of-the-art algorithms.

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“…Beyond the radar architecture, the processing of experimental data plays an important role. In most processing approaches, the goal is to localize hidden targets or produce a qualitative image of the indoor scenario [1][2][3][4][5][6], returning information about the target's shape or the presence of interfaces. Accurate forward scattering approaches may improve the reconstruction capabilities of radar surveys.…”

Section: Introductionmentioning

confidence: 99%

“…Asymptotic techniques are also used for modeling very large regions [10]. However, when applied to imaging approaches, forward solvers are usually implemented through linearized formulations [2,3,[12][13][14] or by using synthetic aperture schemes [4,5,15], thus leading to qualitative images of the target. Techniques to improve the spatial resolution [16,17] and human discrimination [18,19] have also been proposed, through frequency spectral analysis.…”

Section: Introductionmentioning

confidence: 99%

Through-the-Wall Microwave Imaging: Forward and Inverse Scattering Modeling

Fedeli

Pastorino

Ponti

et al. 2020

Sensors

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The imaging of dielectric targets hidden behind a wall is addressed in this paper. An analytical solver for a fast and accurate computation of the forward scattered field by the targets is proposed, which takes into account all the interactions of the electromagnetic field with the interfaces of the wall. Furthermore, an inversion procedure able to address the full underlying non-linear inverse scattering problem is introduced. This technique exploits a regularizing scheme in Lebesgue spaces in order to reconstruct an image of the hidden targets. Preliminary numerical results are provided in order to initially assess the capabilities of the developed solvers.

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All-Directions Through-the-Wall Imaging Using a Small Number of Moving Omnidirectional Bi-Static FMCW Transceivers

Cited by 28 publications

References 20 publications

Multi-Sensor Fusion in Automated Driving: A Survey

Multi-Sensor Fusion in Automated Driving: A Survey

Through-the-Wall Image Reconstruction via Reweighted Total Variation and Prior Information in Radio Tomographic Imaging

Through-the-Wall Microwave Imaging: Forward and Inverse Scattering Modeling

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