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

Author

Wachowski, Neil ; Azimi-Sadjadi, Mahmood R.

Author_Institution

Dept. of Electr. & Comput. Eng., Colorado State Univ., Fort Collins, CO, USA

fYear

2009

fDate

14-19 June 2009

Firstpage

3232

Lastpage

3239

Abstract

This paper presents a new method for multiaspect/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.

Keywords

backpropagation; decision making; neural nets; probability; sensor fusion; signal classification; sonar signal processing; sonar tracking; target tracking; back-propagation neural network; buried object scanning sonar database; decision making; likelihood-based decision feedback system; multiaspect classification fusion method; ping classification; probabilistic neural network; underwater target classification problem; Buried object detection; Collaboration; Feature extraction; Feedback; Neural networks; Neurofeedback; Object oriented databases; Sonar applications; Sonar measurements; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2009. IJCNN 2009. International Joint Conference on

Conference_Location

Atlanta, GA

ISSN

1098-7576

Print_ISBN

978-1-4244-3548-7

Electronic_ISBN

1098-7576

Type

conf

DOI

10.1109/IJCNN.2009.5178634

Filename

5178634