Simulation and Testing Results for a Sub-Bottom Imaging Sonar

TL;DR

This paper presents the development, simulation, and initial testing of a sub-bottom imaging sonar system capable of detecting buried UXO using synthetic aperture imagery and hybrid modeling techniques.

Contribution

It introduces a hybrid modeling approach for synthetic data generation and adapts signal processing algorithms for 3D imaging of buried objects.

Findings

Buried targets were successfully detected in initial tests.

Hybrid models enabled flexible simulation of environmental conditions.

The sonar system demonstrated potential for UXO detection in real environments.

Abstract

The problem of detecting buried unexploded ordnance (UXO) is addressed with a sensor deployed from a shallow-draft surface vessel. This sonar system produces three dimensional synthetic aperture sonar (SAS) imagery of both surficial and buried UXO across a range of environments. The sensor's hardware design was based in part upon data created using a hybrid modeling approach that combined results from separate environmental scattering and target scattering models. This hybrid model produced synthetic sensor data where the sensor/environment/target space could be modified to explore the expected operating conditions. The simulated data were also used to adapt a set of existing signal processing algorithms for formation of three-dimensional acoustic imagery. Recently, the sonar system has been integrated to a test platform, and experiments have been conducted at a trial site in the Foster Joseph Sayers Reservoir near Howard, PA. This test site has been prepared with several buried man-made objects. Initial results show that fully buried targets can be detected.