Synthetic aperture 3D buried object imaging

We present compressed sensing (CS) synthetic aperture radar (SAR) moving target imaging in the presence of dictionary mismatch. Unlike existing work on CS SAR moving target imaging, we analyze the sensitivity of the imaging process to the mismatch and present an iterative scheme to cope with dictionary mismatch. We analyze and investigate the effects of mismatch in range and azimuth positions, as well as range velocity. The analysis reveals that the reconstruction error increases with the mismatch and range velocity mismatch is the major cause of error. Instead of using traditional Laplacian prior (LP), we use Gaussian-Bernoulli prior (GBP) for CS SAR imaging mismatch. The results show that the performance of GBP is much better than LP. We also provide the Cramer-Rao Bounds (CRB) that demonstrate theoretically the lowering of mean square error between actual and reconstructed result by using the GBP. We show that a combination of an upsampled dictionary and the GBP for reconstruction can deal with position mismatch effectively. We further present an iterative scheme to deal with the range velocity mismatch. Numerical and simulation examples demonstrate the accuracy of the analysis as well as the effectiveness of the proposed upsampling and iterative scheme.

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OPTIMIZED UWB SIGNAL TO SHALLOW BURIED OBJECT IMAGING

Progress In Electromagnetics Research Letters ◽

10.2528/pierl17091506 ◽

2018 ◽

Vol 72 ◽

pp. 7-10 ◽

Cited By ~ 6

Author(s):

Ali Gharamohammadi ◽

Yaser Norouzi ◽

Hassan Aghaeinia

Keyword(s):

Buried Object ◽

Object Imaging

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Synthetic Aperture Processing of Buried Object Scanning Sonar Data

Proceedings of OCEANS 2005 MTS/IEEE ◽

10.1109/oceans.2005.1640098 ◽

2006 ◽

Cited By ~ 13

Author(s):

S.G. Schock ◽

J. Wulf ◽

G. Quentin ◽

J. Sara

Keyword(s):

Synthetic Aperture ◽

Buried Object

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A synthetic aperture parametric sonar for buried object detection

The Journal of the Acoustical Society of America ◽

10.1121/1.417765 ◽

1996 ◽

Vol 100 (4) ◽

pp. 2636-2636 ◽

Cited By ~ 2

Author(s):

Stefano Fioravanti ◽

Alain Maguer ◽

Arne Lovik

Keyword(s):

Object Detection ◽

Synthetic Aperture ◽

Buried Object ◽

Parametric Sonar

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GPR Signal Processing with Geography Adaptive Scanning using Vector Radar for Antipersonal Landmine Detection

International Journal of Advanced Robotic Systems ◽

10.5772/5696 ◽

2007 ◽

Vol 4 (2) ◽

pp. 22 ◽

Cited By ~ 2

Author(s):

Toshio Fukuda ◽

Yasuhisa Hasegawa ◽

Yasuhiro Kawai ◽

Shinsuke Sato ◽

Zakarya Zyada ◽

...

Keyword(s):

Ground Penetrating Radar ◽

Ground Surface ◽

Landmine Detection ◽

Image Objects ◽

Kirchhoff Migration ◽

Surface Image ◽

Buried Object ◽

Ground Penetrating ◽

The Relationship ◽

Object Imaging

Ground Penetrating Radar (GPR) is a promising sensor for landmine detection, however there are two major problems to overcome. One is the rough ground surface. The other problem is the distance between the antennas of GPR. It remains irremovable clutters on a sub-surface image output from GPR by first problem. Geography adaptive scanning is useful to image objects beneath rough ground surface. Second problem makes larger the nonlinearity of the relationship between the time for propagation and the depth of a buried object, imaging the small objects such as an antipersonnel landmine closer to the antennas. In this paper, we modify Kirchhoff migration so as to account for not only the variation of position of the sensor head, but also the antennas alignment of the vector radar. The validity of this method is discussed through application to the signals acquired in experiments.

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