Title :
Automatic classification of underwater sonar signals
Author :
Hashem, Hassan Fahmy
Author_Institution :
Alexandria High Inst. of Technol., Egypt
Abstract :
Much work has been performed recently in the area of automatic recognition of the sonar signals to reduce the operator load when confronted with many beams of data concurrently. In this paper we applied and compared different feature extraction methods such as the wavelet basis method, discrete cosine transform method (DCT) and linear prediction method (LP) and then we applied neural network techniques to the processing of sonar signal classification.
Keywords :
discrete cosine transforms; feature extraction; neural nets; prediction theory; signal classification; sonar signal processing; wavelet transforms; DCT; LP; automatic classification; automatic recognition; discrete cosine transform method; feature extraction methods; linear prediction method; neural network; operator load; sonar signal classification; underwater sonar signals; wavelet basis method; Artificial neural networks; Discrete cosine transforms; Discrete wavelet transforms; Feature extraction; Neural networks; Pattern classification; Prediction methods; Shape; Signal processing; Sonar applications;
Conference_Titel :
Neural Network Applications in Electrical Engineering, 2004. NEUREL 2004. 2004 7th Seminar on
Print_ISBN :
0-7803-8547-0
DOI :
10.1109/NEUREL.2004.1416552
