Proud Target Classification Based on Multiple Aspect Low Frequency Response
Abstract
The aspect dependence of the acoustic signature has been demonstrated to be an essential indicator to discriminate between man-made and natural underwater objects. A classification method has been defined using the variation with incidence angle of the acoustic waves scattered by an elastic object. As the experiment conducted in a basin on free-field cylinders produced encouraging results, more realistic acoustic measurements were conducted on natural and manufactured objects positioned on the seabed. The external shape, extracted from a reflection map reconstructed by tomography, allows selection of candidate objects for detailed analysis of their scattering properties. The resonance scattering analysis, limited to selected aspects in its original version (e.g., broadside for a cylindrical shape), has been extended to incorporate aspect-varying features. The variation with incidence of the acoustic wave diffracted by object discontinuities also has been introduced. This report discusses the results from the SACLANTCEN-NRL TASCOE (TArget Scattering in COntrolled Environment) experiment conducted in water depth of 15 meters at Marciana Marina (Elba, Italy) in October 1998. The scope of the TASCOE experiment was to acquire the acoustic response of proud objects over a broad range of azimuth angles. Data analysis shows that the aspect dependence of the acoustic waves scattered by elastic objects (ka = 2-20) allows clear discrimination between manufactured and natural objects. To provide elements of comparison with more conventional techniques, a multiple aspect automatic classification algorithm was applied to the high frequency (325 kHz) images of the same objects. The low (8kHz ricker pulse) and high frequency target responses complement each other to provide better characterization of mine-like objects. (2 tables, 16 figures, 18 refs.)
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 01, 2001
- Accession Number
- ADA416954
Entities
People
- A. Maguer
- A. Tesei
- B. Houston
- B. Zerr
- P. A. Sletner
Organizations
- SACLANT ASW Research Centre