Several imaging algorithms for synthetic aperture sonar and forward looking gap‐filler in real‐time and post‐processing on IXSEA's “Shadows” sonar

2008 ◽  
Vol 123 (5) ◽  
pp. 3906-3906 ◽  
Author(s):  
Frédéric Jean
Keyword(s):  
Real Time ◽  
Synthetic Aperture ◽  
Post Processing ◽  
Synthetic Aperture Sonar ◽  
Imaging Algorithms ◽  
Forward Looking

Real-time synthetic aperture sonar processing system

1996 ◽  
Vol 143 (3) ◽  
pp. 169 ◽  
Author(s):  
A.E. Adams ◽  
M.A. Lawlor ◽  
V.S. Riyait ◽  
O.R. Hinton ◽  
B.S. Sharif
Keyword(s):  
Real Time ◽  
Processing System ◽  
Synthetic Aperture ◽  
Synthetic Aperture Sonar

Method of Extracting Real-time Bathymetry Information of Towed Interferometric Synthetic Aperture Sonar System

2021 ◽  
Vol 4 (1) ◽  
pp. 61-66
Author(s):  
Oh-Rum Cha ◽  
Seung-Soo Park ◽  
Jong-Gwon Choi ◽  
Young-Seok Oh
Keyword(s):  
Real Time ◽  
Synthetic Aperture ◽  
Synthetic Aperture Sonar ◽  
Sonar System

Real-Time Modeling of Forward-Looking Synthetic Aperture Ground Penetrating Radar Scattering From Rough Terrain

2019 ◽  
Vol 57 (5) ◽  
pp. 2754-2765 ◽  
Author(s):  
Mohammad M. Tajdini ◽  
Borja Gonzalez-Valdes ◽  
Jose A. Martinez-Lorenzo ◽  
Ann W. Morgenthaler ◽  
Carey M. Rappaport
Keyword(s):  
Real Time ◽  
Ground Penetrating Radar ◽  
Synthetic Aperture ◽  
Rough Terrain ◽  
Time Modeling ◽  
Radar Scattering ◽  
Ground Penetrating ◽  
Forward Looking

Real-time synthetic aperture sonar imaging using a parallel architecture

1995 ◽  
Vol 4 (7) ◽  
pp. 1010-1019 ◽  
Author(s):  
V.S. Riyait ◽  
M.A. Lawlor ◽  
A.E. Adams ◽  
O. Hinton ◽  
B. Sharif
Keyword(s):  
Real Time ◽  
Parallel Architecture ◽  
Synthetic Aperture ◽  
Synthetic Aperture Sonar ◽  
Sonar Imaging

Shadow Detecting and Shadow Interpolation Algorithm for InSAS

2012 ◽  
Vol 605-607 ◽  
pp. 2121-2125 ◽  
Author(s):  
Sen Zhang ◽  
Ming Chen ◽  
Jin Song Tang
Keyword(s):  
Real Time ◽  
Interpolation Method ◽  
Linear Interpolation ◽  
Synthetic Aperture ◽  
Interpolation Algorithm ◽  
Synthetic Aperture Sonar ◽  
Shadow Area ◽  
Linear Interpolation Method

The shadow detecting algorithm based on the coherence and the Sigma filter is used to pick up the shadow of interferometric synthetic aperture sonar (InSAS), which can eliminate small separated shadow areas. To solve the problems such as great computer complexity of traditional Shepard interpolation method and large fluctuant of linear interpolation method for the large shadow area, an improved Shepard interpolation method is proposed. Interpolation boundary is picked up by using diffuse search, and interpolation source is adaptively chosen according to the size of shadow area. The method carries out a perfect tradeoff between performance and computer speed. Lake trial dataset is used to validate the performance of proposed method. The results indicate that the proposed method can eliminate the fluctuant from the linear interpolation method and can process in real time in the InSAS system.

scholarly journals Compressive Underwater Sonar Imaging with Synthetic Aperture Processing

2021 ◽  
Vol 13 (10) ◽  
pp. 1924
Author(s):  
Ha-min Choi ◽  
Hae-sang Yang ◽  
Woo-jae Seong
Keyword(s):  
Experimental Data ◽  
Synthetic Aperture ◽  
High Concentration ◽  
Side Scan Sonar ◽  
Synthetic Aperture Sonar ◽  
Underwater Image ◽  
Line Array ◽  
High Level ◽  
Imaging Algorithms ◽  
Imaging Performance

Synthetic aperture sonar (SAS) is a technique that acquires an underwater image by synthesizing the signal received by the sonar as it moves. By forming a synthetic aperture, the sonar overcomes physical limitations and shows superior resolution when compared with use of a side-scan sonar, which is another technique for obtaining underwater images. Conventional SAS algorithms require a high concentration of sampling in the time and space domains according to Nyquist theory. Because conventional SAS algorithms go through matched filtering, side lobes are generated, resulting in deterioration of imaging performance. To overcome the shortcomings of conventional SAS algorithms, such as the low imaging performance and the requirement for high-level sampling, this paper proposes SAS algorithms applying compressive sensing (CS). SAS imaging algorithms applying CS were formulated for a single sensor and uniform line array and were verified through simulation and experimental data. The simulation showed better resolution than the ω-k algorithms, one of the representative conventional SAS algorithms, with minimal performance degradation by side lobes. The experimental data confirmed that the proposed method is superior and robust with respect to sensor loss.

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