Adaptive feature mapping for underwater target classification

In this paper, a new feature mapping scheme is presented to cope with environmental and target signature changes for 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 system is the adaptive feature mapping subsystem that minimizes the classification error of the classifier. The extracted feature vector is mapped by the resultant transformation 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 classifier. The test results on 40 kHz linear FM acoustic backscattered data collected for six different objects are presented The effectiveness of the adaptive system vs. nonadaptive one is demonstrated for several signal-to-noise ratio (SNR) conditions