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Underwater target classification in changing environments using adaptive feature mapping schemes

A new adaptive feature mapping scheme is presented in this paper to cope with environmental and target signature changes in underwater target classification. A wavelet packet-based feature extraction scheme is used in conjunction with the linear prediction coding (LPC) scheme as the front-end processor. The core of the adaptive classification system is the adaptive feature mapping sub- system that minimizes the classification error of the classifier. The extracted feature vector is mapped by the resultant feature mapping matrix in such a way that the mapped version remains invariant to the environmental and sensory changes. The feedback to the adaptation mechanism is provided by a K-nearest neighbor (K-NN) classifier. In order to alleviate problems caused by poorly scaled features, a revised K-NN based on the scaled Euclidean distance was adopted. Two error criteria were used in the adaptive system, one is the least squares (LS) error criterion and the other is 2D sigmoid cost function. Those two criteria were combined together to offer a better performance. The test results on 40KHz sigmoid cost function. Those two criteria were combined together to offer a better performance. The test result on 40KHz linear FM acoustic backscattered data collected for six different objects are presented. The effectiveness of the adaptive system vs. non- adaptive system is demonstrated when the signal-to- reverberation ratio in the environment is varying.