A likelihood-based decision feedback system for multi-aspect classification of underwater targets

Authors:
Neil Wachowski;Mahmood R. Azimi-Sadjadi
Affiliations:
Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO;Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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Abstract

This paper presents a new method for multi-aspect/ping classification of underwater objects using sonar data. This system uses decision feedback to form a likelihood ratio for making a high confidence final decision based not only upon the data of the current ping but also the final decisions made at several previous pings. The system is then applied to an underwater target classification problem. Test results on a buried object scanning sonar (BOSS) database collected for different objects and in different conditions show the promise of the proposed method for multi-aspect underwater target discrimination.