Title :
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
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;
Conference_Titel :
Neural Networks, 2009. IJCNN 2009. International Joint Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
978-1-4244-3548-7
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2009.5178634
