Military training and weapons testing activities in the past have left a legacy of unexploded ordnance (UXO) at a number of sites. Particularly difficult is the characterization and remediation of those sites where UXO is found in underwater environments. Many active and former military installations have ordnance ranges and training areas that include adjacent water environments (e.g., ponds, lakes, rivers, estuaries, and coastal ocean areas). Presently, there exists no effective capability to survey these underwater areas and map the location of UXO for site characterization and little understanding of the UXO or clutter characteristics from which to establish performance requirements.

The objectives of this project were to evaluate the effectiveness of synthetic aperture sonar for detection and classification of underwater munitions, characterize the influence of sediment heterogeneity on buried target detection and discrimination, identify processing methods to improve performance of detection systems, and provide recommendations for future algorithm and system development.

Sonar is a natural candidate for UXO detection in shallow water due to its wide-area surveillance capability and target sensitivity. However, sonar signature interpretation is complicated by a number of factors including natural and man-made clutter, data dependence on viewing geometry and target state, environmental heterogeneity and wave propagation complexity, coupling of target response with the environment, and sensor positioning and motion compensation requirements. This study addressed these factors with a special concentration on clutter and environmental heterogeneity and wave propagation complexity, using these findings for improved design of classification algorithms.

Low-frequency data collected from the Buried Object Scanning Sonar (BOSS) was processed into three-dimensional (3D) imagery using beamforming, and target/clutter classifiers that use 3D features extracted from this imagery were developed. The principal sonar data sources were BOSS deployments at various shallow water sites. Morphological processing was applied to the derived imagery for feature input into a relevance vector machine classifier. Since ground truth was available, performance metrics in the form of ROC curves were computed. To enable a systematic understanding of the influence of the environment on target responses, researchers developed a poroelastic spectral element method for BOSS data simulations using two-dimensional and 3D models. The classification results established that buried targets have a high probability of detection with the BOSS. However, features from target imagery responses are easily confused with those of clutter and munitions debris due to their incomplete separation. A theoretical development for the estimation of structural acoustic resonance features from BOSS-like data was created. Researchers observed that future classification performance gains with the sonar modality will likely rely on the combined use of imagery- and resonance-based features.

The data products, target and environment models, and target signatures (and features) will provide a resource for design and development of future target recognition systems.