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2 August 1999 Adaptive underwater target classification system
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Abstract
This paper presents a new adaptive underwater target classification system for in situ robust classification ins presence of environmental and target signature changes. A wavelet packet-based feature extraction scheme is used as a front-end processor to extract pertinent features in each subband. The extracted features correspond to the linear predictive coding coefficients of the signals in each subband. The heart of the system is an adaptive feature mapping subsystem that maps the original feature space in such a way that the mapped feature vector remains invariant to the environmental and sensory conditions. The goal is to minimize the classification error of the neural network classifier on the changing data. The feedback to the adaptation mechanisms is provided by a K-nearest neighbor classifier that can also be updated in situ in face of changing environment. Preliminary results on broadband acoustic backscattered signals collected for six different objects are obtained which reveal the effectiveness of the system when compared with the non-adaptive system.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mahmood R. Azimi-Sadjadi, De Yao, and Gerald J. Dobeck "Adaptive underwater target classification system", Proc. SPIE 3710, Detection and Remediation Technologies for Mines and Minelike Targets IV, (2 August 1999); https://doi.org/10.1117/12.357092
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