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Feature selection, adaptive detection/classification, and beam forming for mine avoidance sonar

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N65538-04-M-0067
Agency Tracking Number: N041-072-0322
Amount: $70,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N04-072
Solicitation Number: 2004.1
Timeline
Solicitation Year: 2004
Award Year: 2004
Award Start Date (Proposal Award Date): 2004-04-21
Award End Date (Contract End Date): 2004-10-21
Small Business Information
8248 Sugarman Drive
La Jolla, CA 92037
United States
DUNS: 154269187
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Richard Altes
 Research Engineer
 (858) 453-4406
 altes@att.net
Business Contact
 Richard Altes
Title: President
Phone: (858) 453-4406
Email: altes@att.net
Research Institution
N/A
Abstract

New techniques are proposed for clutter and multipath suppression, reliable adaptive detection/classification, and beam forming. Generalized (multivariate) prediction-subtraction is proposed for clutter reduction. Innovative blind deconvolution algorithms are proposed for multipath suppression. Adaptive maximum likelihood beam forming, beam deconvolution, and parametric sonar are considered for effective beam narrowing. Proposed detection/classification algorithms use "feature-grams," a generalization of time-frequency distributions that includes many range-varying parameter representations, such as incomplete synthetic aperture feature images from forward-looking echo data. A novel, totally automatic method discovers the best features for a reduced-dimensional feature space from observations of design set feature-grams. The resulting feature space data representation is more meaningful to a sonar operator than the usual principal component representation. New feature-grams and associated classification algorithms are demonstrated via single-echo classification of wideband, wide-beam sonar data from targets in clutter. Segmented feature-grams are proposed for extended hidden Markov models that adaptively classify single-echo or multi-echo data. An adaptive classifier learns to improve its performance using unlabeled echoes from mine-like objects and clutter that are not included in the original design set. Reliable adaptive feature extraction and classification are needed for real-world operation against environments and objects that cannot all be included in an initial design set.

* Information listed above is at the time of submission. *

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