Sensor Phenomenology and Feature Development for Improved Sonar-based Detection & Classification of Underwater UXO
Abstract
This effort has examined the problem of detection and classification of buried munitions in underwater environments. We have focused on the use of low frequency sonar since high frequency acoustic waves are strongly attenuated by sediments. The focus of this effort has been to process low-frequency data collected from the Buried Object Scanning Sonar (BOSS) into 3D imagery using beamforming, and to develop target/clutter classifiers that use 3D features extracted from this imagery. The principal sonar data sources are 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, it was possible to compute performance metrics in the form of ROC curves. To enable a systematic understanding of the influence of the environment on target responses, we have developed a poroelastic spectral element method for BOSS data simulations using 2D and 3D models. The classification results establish that buried targets have a high probability of detection with the Buried Object Scanning Sonar. However, features from target imagery responses are easily confused with those of clutter and munitions debris due to their incomplete separation. Small subsets of possible imagery features show the best performance, and various examples are shown. We provide a theoretical development for the estimation of structural acoustic resonance features from BOSS-like data. Future classification performance gains with the sonar modality will likely rely on the combined use of imagery- and resonance-based features.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 01, 2009
- Accession Number
- ADA520643
Entities
People
- Eugene Lavely
Organizations
- BAE Systems Inc.