Electromagnetic Subsurface Remote Sensing

Chen, S.Y.; Chew, Weng Cho; Santos, Vinicius Rocha; Sainath, Kamalesh; Teixeira, Fernando L.

doi:10.1002/047134608x.w3602.pub2

Cited by 10 publications

(2 citation statements)

References 97 publications

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“…In general, TR invariance can be exploited for detection and localization of obscured targets in noisy and rich-scattering environments, with high resolution. Timereversal (TR) techniques were first developed for acoustics (Fink et al, 1989) (Fink, 1992), being later successfully used in several applications including non-destructive testing and evaluation (Liu et al, 2014), sound quality enhancement (Lin & Too, 2014), atmospherics studies (Mora et al, 2012), subsurface geophysics (Fink, 2006;Leuschen & Plumb, 2001;Saillard et al, 2004;Cresp et al, 2008;Artman et al, 2010;Foroozan & Asif, 2010;Yavuz et al, 2014;Chen et al, 2016), microwave remote sensing (Reyes-Rodríguez et al, 2014), wireless communications (Fouda et al, 2012) and medicine 1 Backpropagation can be effected either physically by transmitting the time-reversed signals into the original medium, or synthetically by means of a forward simulation engine, such as the finite-difference time-domain (FDTD) method. Synthetic backpropagation is done for imaging purposes, as considered here.…”

Section: Introductionmentioning

confidence: 99%

Application of time-reversal-based processing techniques to enhance detection of GPR targets

Santos

Teixeira

2017

Journal of Applied Geophysics

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In this paper we analyze the performance of time-reversal (TR) techniques in conjunction with various Ground Penetrating Radar (GPR) pre-processing methods aimed at improving detection of subsurface targets. TR techniques were first developed for ultrasound applications and, by exploiting the invariance of the wave equation under time reversal, can yield features such as superresolution and statistical stability. The TR method was examined here using both synthetic and actual GPR field data under four different pre-processing strategies on the raw data, namely: mean background removal, eigenvalue background removal, a sliding-window space-frequency technique, and a noise-robust spatial differentiator along the scan direction. Depending on the acquisition mode, it was possible to determine with good precision the position and depth of the studied targets as well as, in some cases, to differentiate the targets from nearby clutter such as localized geological anomalies. The proposed methodology has the potential to be useful when applied to GPR data for the detection of buried targets.

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Section: Introductionmentioning

confidence: 99%

Application of time-reversal-based processing techniques to enhance detection of GPR targets

Santos

Teixeira

2017

Journal of Applied Geophysics

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“…Ground penetrating radar (GPR) finds a large number of applications related to detection and imaging of subsurface targets and anomalies such as underground utilities, pipes, chemical spills, groundwater levels, etc. Historically, GPR data has been typically analyzed and interpreted based on a "visual" analysis of the radargram [1], [2]; however, this analysis is able to provide reliable interpretation only in simple scenarios. In order to improve the interpretability of GPR data, inverse scattering and migration algorithms [3]- [8], including microwave tomography (MT) techniques [9]- [11], have also been used in different scenarios related to GPR surveys.…”

Section: Introductionmentioning

confidence: 99%

A Controlled-Site Comparison of Microwave Tomography and Time-Reversal Imaging Techniques for GPR Surveys

Santos¹,

Almeida²,

Porsani³

et al. 2017

Preprint

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This paper provides a comparative study between microwave tomography and synthetic 14 time-reversal imaging techniques as applied to ground penetrating radar (GPR) surveys. The 15 comparison is carried out by processing experimental data collected at a controlled test site, with 16 various types of buried targets at given subsurface depths and representative soil conditions. It is 17shown that the two techniques allow us to obtain complementary information about position, 18 depth and size of the targets from a single GPR survey. 19

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Consensus in Multi‐Agent Systems

Proskurnikov

Cao

2016

Wiley Encyclopedia of Electrical and Electronics Engineering

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Many cooperative behaviors of multi‐agent teams emerge from local interactions among the agents, where an agent interacts with a few “adjacent” teammates, but has no information about the remaining agents. For instance, the self‐organization of many biological populations –including swarms of insects, flocks of birds, and schools of fish –are based on such local interaction rules: the motion and decisions of an individual agent are determined by the behavior of its nearest neighbors in the population. A special case of multi‐agent coordination is consensus , that is, the agreement of agents on some quantity of interest or, more generally, the full or partial synchronization of their state trajectories. Establishing consensus is a “benchmark” problem in multi‐agent systems study, which allows to reveal the main principles of multi‐agent coordination and, in particular, the role of the system's interaction graph (or topology). Consensus lies in the heart of many natural phenomena (e.g., synchronous oscillation of neural cells, which maintains a stable heart rhythm) and engineering designs (e.g., attitude synchronization of satellites). In this article, we give a brief review of distributed consensus algorithms, focusing on the basic ideas and relevant mathematical theory, in particular, graph‐theoretic methods.

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Electromagnetic Subsurface Remote Sensing

Cited by 10 publications

References 97 publications

Application of time-reversal-based processing techniques to enhance detection of GPR targets

Application of time-reversal-based processing techniques to enhance detection of GPR targets

A Controlled-Site Comparison of Microwave Tomography and Time-Reversal Imaging Techniques for GPR Surveys

Consensus in Multi‐Agent Systems

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