Underwater target classifier using modified Kaiser-Bessel window

Underwater target classification has got numerous applications in ocean systems and technologies. The selection of suitable source specific features in a classifier system is one of the major factors determining the efficiency and efficacy of the classifier. The spectral features, when suitably modified, can provide certain essential clues suitable for the design of underwater signal classifiers. In this paper, a non-stochastic underwater target classifier, making use of an efficient feature set based on modified Kaiser-Bessel window, operating in the frequency domain is proposed. The proposed classifier is making use of a Matching Parameter which is a functional measure of the Mahalanobis and Euclidean distances and utilizes an algorithmic vector quantization approach as well for cluster formation. The system performance has been studied and fairly acceptable success rates have been obtained for the proposed underwater target classifier, making use of a modified Kaiser-Bessel window.