CLEAN Technique to Classify and Detect Objects in Subsurface Imaging

Karpat, Esin

doi:10.1155/2012/917248

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…A recently introduced GrGPR, virtual tool is used to generate synthetic data for sample scenarios [13][14][15]. In the simulations 50 transmitter/reciever antennas are placed over terrain and activated sequentially as in SAR principle or we can assume that a transmitter/reciever antenna pair is activated in 50 different positions over terrain (Fig.…”

Section: Methodsmentioning

confidence: 99%

Subsurface Imaging of Dielectric Objects Buried under Rough Surface

2014

2nd International Conference on Research in Science, Engineering and Technology (ICRSET’2014), March 21-22, 2014 Dubai (UAE)

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In this paper, a distance measurement based reflection point terrain estimation method (RPTEM) for characterizing twodimensional (2-D) rough surfaces is presented. The method is based on the analysis of time-domain field data obtained by GPR system with Synthetic Aperture Radar (SAR) scan over 2-D rough ground surfaces. The distance from each antenna position to the ground surface is established from the late time responses of each antenna. The distance information extracted form the reflected signal is used to to create an estimate of the rough ground. A circle is considered with a corresponding radius, which is the distance information, with the antenna location at its center. An outline of the terrain is obtained through the overlapping circles at neighboring antennas. The terrain profiles obtained via both SAR-processing and the RPTEM are discussed and compared with the actual geometry. The results show good agreement between the imagery of the surface height distribution obtained by RPTEM, SAR processing and the actual geometry of the 2-D rough surfaces. Time consumed for image reconstruction is discussed for each method.

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Section: Methodsmentioning

confidence: 99%

Subsurface Imaging of Dielectric Objects Buried under Rough Surface

2014

2nd International Conference on Research in Science, Engineering and Technology (ICRSET’2014), March 21-22, 2014 Dubai (UAE)

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“…A number of methods of signal processing have been put forward for improving landmine GPR system performance, including forward scattering radar classification [29], background subtraction, CLEAN algorithm [30], Kalman filters [31], likelihood ratio test [32], wavelet packet decomposition [33], and additional two-dimensional filtering [34]. The majority of these methods depends on estimating the background signal through Green's function or calculating a mean value for the unprocessed data collected by GPR and then subtracting the estimated background signal from the received signal.…”

Section: Related Workmentioning

confidence: 99%

UAV Tomographic Synthetic Aperture Radar for Landmine Detection

Almutiry

2020

Eng. Technol. Appl. Sci. Res.

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The development of the Unmanned Aerial Vehicle (UAV) and communication systems contributed to the availability of more applications using UAVs in military and civilians purposes. Anti-personnel landmines deployed by militia groups in conflict zones are a life threat for civilians and need cautious handling while removing. The UAV Tomographic Synthetic Aperture Radar (TSAR) can reconstruct three-dimension images of the investigation domain to prescreen nonmetallic landmines. A nonmetallic landmine cannot be detected using conventional ground penetrating radars when the scattering field is undetected due to the dielectric permittivity. In this paper, imaging the underground for detecting landmine using TSAR is proposed. The TSAR has the capability of prosing the data in discrete mode regardless of the altitude of UAV’s radar. A landmine is always buried less than a feet depth. L-band frequency is used to provide high resolution and to penetrate deep in dry soil. More than one UAVs are used to multistatic scan the investigation space. The geometric diversity of multistatic distribution of the sensors will provide more information about the buried nonmetallic landmines, certain features, and their location. The data collected from the sensors will align with the geolocation data obtained from the UAV’s system for processing. Dynamic flying can be used to predict the electromagnetic response of the scattering field to create a dynamic matching filter using the Green’s function under first-order Born approximation. The occurring air-soil interference has been removed as an unwanted reflection from the ground while keeping the signal coming from underground. Using the Born approximation assumption created an ill-posed linear system solved by the Conjugate Gradient algorithm. Simulation results are presented to validate the method.

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High resolution imaging for MIMO forward looking ground penetrating radar

Ojowu

et al. 2014

2014 48th Asilomar Conference on Signals, Systems and Computers

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CLEAN Technique to Classify and Detect Objects in Subsurface Imaging

Cited by 5 publications

References 28 publications

Subsurface Imaging of Dielectric Objects Buried under Rough Surface

Subsurface Imaging of Dielectric Objects Buried under Rough Surface

UAV Tomographic Synthetic Aperture Radar for Landmine Detection

High resolution imaging for MIMO forward looking ground penetrating radar

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