Title :
Classification of sonar signals using Bayesian networks
Author :
Larkin, Michael J.
Author_Institution :
Naval Underwater Syst. Center, Newport, RI, USA
Abstract :
A pertinent issue in the development of automated classifiers is the combination of information extracted from the data with a priori information that we may have about the classes in questions. Incorporating prior knowledge is particularly useful if the data is incomplete or imprecise. Bayesian networks offer an ideal representation for the combination of a priori knowledge with data. This paper discusses the implementation of a Bayesian network classifier for the purpose of classifying underwater mines, using knowledge that is known by experts (sonar operators) about the distinguishing characteristics of mines, such as size, shape, shadow and resonance.
Keywords :
Bayes methods; expert systems; feature extraction; military computing; object detection; pattern classification; sonar signal processing; unsupervised learning; Bayesian network classifier; a priori information; automated classifiers; expert knowledge; feature extraction; incomplete data; resonance; shadow; shape; size; sonar signals classification; underwater mines; unsupervised methods; Acoustic reflection; Acoustic signal detection; Bayesian methods; Data mining; Object detection; Resonance; Shape; Sonar detection; Underwater tracking; Working environment noise;
Conference_Titel :
Signals, Systems & Computers, 1997. Conference Record of the Thirty-First Asilomar Conference on
Conference_Location :
Pacific Grove, CA, USA
Print_ISBN :
0-8186-8316-3
DOI :
10.1109/ACSSC.1997.680564
