INTERNATIONAL SYMPOSIUM ON INFORMATION TECHNOLOGY IN OCEANOGRAPHY (ITO-98) GOA, INDIA 12-16 OCTOBER 1998

Classification of underwater contacts from sonar information is predominantly carried out by human operators till date. However with improved sensor technology and increasingly sophisticated threat scenario, the task of sonar classification is becoming more and more difficult on account of increased volume and complexity of the data available for processing. Future scenario incorporating stealthy underwater vehicles with very low level signatures, coupled with the ever increasing sophistication of the threat , improved sensor sensitivity and diversity would add on to the ambiguity within the sonar picture

This paper discusses the design and development of a three layered ,feed forward, back propagation trained , neural network based sonar classifier. The classifier will be able to indicate the presence of a particular class of under water vehicle, while rejecting self and ambient noise and false targets. The concept of the designed classifier system is the exploration of the pattern recognition capabilities of Artificial Neural Network to emulate abilities of the human operator and in particular the ability to form a classifier decision from the recognition of discriminating pattern in the information which match knowledge from previous experiences. The design would combine advantages of traditional signal processing techniques with those of neural networks.

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Copyright© National Institute Of Oceanography, Goa,India,1998
updated on 7th July'98