scholarly journals Synthetic Aperture Sonar (SAS) without Navigation: Scan Registration as Basis for Near Field Synthetic Imaging in 2D

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4440
Author(s):  
Heiko Bülow ◽  
Andreas Birk

Sonars are essential for underwater sensing as they can operate over extended ranges and in poor visibility conditions. The use of a synthetic aperture is a popular approach to increase the resolution of sonars, i.e., the sonar with its N transducers is positioned at k places to generate a virtual sensor with kN transducers. The state of the art for synthetic aperture sonar (SAS) is strongly coupled to constraints, especially with respect to the trajectory of the placements and the need for good navigation data. In this article, we introduce an approach to SAS using registration of scans from single arrays, i.e., at individual poses of arbitrary trajectories, hence avoiding the need for navigation data of conventional SAS systems. The approach is introduced here for the near field using the coherent phase information of sonar scans. A Delay and Sum (D&S) beamformer (BF) is used, which directly operates on pixel/voxel form on a Cartesian grid supporting the registration. It is shown that this pixel/voxel-based registration and the coherent processing of several scans forming a synthetic aperture yields substantial improvements of the image resolution. The experimental evaluation is done with an advanced simulation tool generating realistic 2D sonar array data, i.e., with simulations of a linear 1D antenna reconstructing 2D images. For the image registration of the raw sonar scans, a robust implementation of a spectral method is presented. Furthermore, analyses with respect to the trajectories of the sensor locations are provided to remedy possible grating lobes due to the gaping positions of the transmitter devices.

2021 ◽  
Author(s):  
Takuma Watanabe ◽  
Hiroyoshi Yamada

<div>*This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.<br></div><div><br></div>In this article, we propose a 3-D synthetic aperture imaging method with spherical antenna scanning to identify scatterers located close to an antenna array, such as fixtures or support of the antenna. Previous studies have shown that 2-D and 3-D synthetic aperture imaging techniques with planar, circular, and cylindrical scanning can successfully reconstruct spatial images of near-field scatterers. The spherical scanning approach considered in this article is expected to improve the 3-D image resolution because more angular diversity can be achieved in the elevation direction. However, as we show in this study, simple extension of the previous techniques to the spherical case results in undesired blur artifacts in the reconstructed image. To overcome this problem, we introduce a correction factor in the image reconstruction. The proposed imaging algorithm is validated by numerical electromagnetic simulation based on the method of moments.


2008 ◽  
Vol 124 (4) ◽  
pp. 2584-2584
Author(s):  
Shawn F. Johnson ◽  
Anthony P. Lyons ◽  
Douglas A. Abraham

2021 ◽  
Author(s):  
Takuma Watanabe ◽  
Hiroyoshi Yamada

<div>*This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.<br></div><div><br></div>In this article, we propose a 3-D synthetic aperture imaging method with spherical antenna scanning to identify scatterers located close to an antenna array, such as fixtures or support of the antenna. Previous studies have shown that 2-D and 3-D synthetic aperture imaging techniques with planar, circular, and cylindrical scanning can successfully reconstruct spatial images of near-field scatterers. The spherical scanning approach considered in this article is expected to improve the 3-D image resolution because more angular diversity can be achieved in the elevation direction. However, as we show in this study, simple extension of the previous techniques to the spherical case results in undesired blur artifacts in the reconstructed image. To overcome this problem, we introduce a correction factor in the image reconstruction. The proposed imaging algorithm is validated by numerical electromagnetic simulation based on the method of moments.


2006 ◽  
Author(s):  
Steven G. Kargl ◽  
Kevin L. Williams ◽  
Eric L. Thoros ◽  
Joseph L. Lopes

1997 ◽  
Author(s):  
Frank Henyey ◽  
Kevin Williams

2020 ◽  
Vol 8 (1) ◽  
pp. 84-90
Author(s):  
R. Lalchhanhima ◽  
◽  
Debdatta Kandar ◽  
R. Chawngsangpuii ◽  
Vanlalmuansangi Khenglawt ◽  
...  

Fuzzy C-Means is an unsupervised clustering algorithm for the automatic clustering of data. Synthetic Aperture Radar Image Segmentation has been a challenging task because of the presence of speckle noise. Therefore the segmentation process can not directly rely on the intensity information alone but must consider several derived features in order to get satisfactory segmentation results. In this paper, it is attempted to use the fuzzy nature of classification for the purpose of unsupervised region segmentation in which FCM is employed. Different features are obtained by filtering of the image by using different spatial filters and are selected for segmentation criteria. The segmentation performance is determined by the accuracy compared with a different state of the art techniques proposed recently.


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