An Efficient Acoustic Scattering Model Based on Target Surface Statistical Descriptors for Synthetic Aperture Sonar Systems

Abstract

Acoustic scattering as the perturbation of an incident acoustic field from an arbitrary object is a critical part of the target-recognition process in synthetic aperture sonar (SAS) systems. The complexity of scattering models strongly depends on the size and structure of the scattered surface. In accurate scattering models including numerical models, the computational cost significantly increases with the object complexity. In this paper, an efficient model is proposed to calculate the acoustic scattering from underwater objects with less computational cost and time compared with numerical models, especially in 3D space. The proposed model, called texture element method (TEM), uses statistical and structural information of the target surface texture by employing non-uniform elements described with local binary pattern (LBP) descriptors by solving the Helmholtz integral equation. The proposed model is compared with two other well-known models, one numerical and other analytical, and the results show excellent agreement between them while the proposed model requires fewer elements. This demonstrates the ability of the proposed model to work with arbitrary targets in different SAS systems with better computational time and cost, enabling the proposed model to be applied in real environment.

Communication-Aware Mobile Relaying via an AUV for Minimal Wait Time: A Broad Learning-Based Solution

“…However, the above algorithms ignore the impact of obstacles on channel distribution. In [5], [6], the Kirchhoff approximation-based numerical methods were developed to capture the impact of obstacles on the channel, however they are not suitable for the large objects due to the huge computational workload. To handle this issue, an analytical method was proposed in [7] to study the scattering of texture details, but it is not suitable for studying the scattering effect of distant obstacles.…”

Efficient Detection of Underwater Natural Gas Pipeline Leak Based on Synthetic Aperture Sonar (SAS) Systems

“…The TEM model calculated acoustic scattering fields based on an efficient non-uniform discretization method using statistical and structural information of target surface texture and led to realistic modeling of underwater targets, such as pipelines, in different SAS systems. The TEM model can be used efficiently in various underwater applications as pipeline modeling in leak detection in different SAS systems [14].…”

“…In the next experiment, gas bubble acoustic scattering in the presence of a pipeline as a background echo is shown as a function of depth (range) and along-track path with the proposed method and the non-coherent real aperture method, which the target scene includes a part of the pipeline and a leak gas bubble in the along-track midway. Pipeline scattering as the background echo is calculated using the TEM acoustic scattering model from a cylinder [14].…”

“…In the proposed method, a coherent combination of gas bubble acoustic scattering in synthetic aperture range migration is used to accurately model gas bubbles in different sizes and ranges. The pipeline acoustic scattering is computed using the Texture Element Model (TEM) [14], which is an accurate and computationless model for computed acoustic scattering of underwater targets, essentially in SAS systems processing. Consequently, the proposed method can distinguish gas leak signals by collecting accurate scattering information in one pass only.…”

Two-Dimensional Space-Variant Motion Compensation Algorithm for Multi-Hydrophone Synthetic Aperture Sonar Based on Sub-Beam Compensation

“…Synthetic aperture sonar (SAS) [1][2][3] is a high-resolution acoustic imaging system mounted on an autonomous underwater vehicle (AUV), remote-operated vehicle (ROV), and unmanned undersea vehicle (UUV) platforms. It is widely applied in underwater topography mapping, small-target detection, and buried object detection [4][5][6][7]. However, the instability of the platform, underwater turbulence, and other factors in the changeable underwater environment easily cause 2-D SV motion errors in the SAS, which vary with the range and azimuth dimensions.…”