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.

Keywords

Underwater acoustic scattering / Synthetic aperture sonar (SAS) / Texture / Local binary pattern (LBP) / Target strength (TS) / Discretization method

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