ABSTRACT
Detection and classification of underwater objects in sonar is a complicated problem, due to various factors such as variations in operating and environmental conditions and the attenuation of the sonar signal in the water column can totally obscure a target-like object. In order to overcome such complicated problems detection and classification system is needed. Among them classification plays a major role in detection. Adaptive Boosting Technique (AdaBoost) is one of the best classifier for classification of the things with minimum error. The aim of the project is to implement AdaBoost technique to classify the given inputs depending on the features that are given to the training data. Using this technique, the signal de-noising is achieved. So this signal de-noising application will be very useful in the underwater target detections where the noise dominance is more.
