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Proceedings Paper

Adaptive underwater target classification with multi-aspect decision feedback
Author(s): Mahmood R. Azimi-Sadjadi; Arta A. Jamshidi; Gerald J. Dobeck
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Paper Abstract

This paper presents a new scheme for underwater target classification in a changing environment. An adaptive target classification system is developed that uses the decision of multiple aspects of the objects. The system employs a decision feedback mechanism to map the changed feature vector to a new feature space familiar to the classifier. Results on an acoustic backscattered data set, namely the 40kHz data collected at Coastal Systems Station are presented. This data set contains returns form six different objects at 72 aspect angles with 5 degrees separation and with varying signal-to-reverberation ratio. The results are then benchmarked with those of a neural network-based multi- aspect fusion system.

Paper Details

Date Published: 18 October 2001
PDF: 10 pages
Proc. SPIE 4394, Detection and Remediation Technologies for Mines and Minelike Targets VI, (18 October 2001); doi: 10.1117/12.445444
Show Author Affiliations
Mahmood R. Azimi-Sadjadi, Colorado State Univ. (United States)
Arta A. Jamshidi, Colorado State Univ. (United States)
Gerald J. Dobeck, Naval Surface Warfare Ctr. (United States)

Published in SPIE Proceedings Vol. 4394:
Detection and Remediation Technologies for Mines and Minelike Targets VI
Abinash C. Dubey; James F. Harvey; J. Thomas Broach; Vivian George, Editor(s)

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