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22 August 2000 Comparison of different neural network classification paradigms for underwater target discrimination
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Abstract
The problem of classification of underwater targets from the acoustic backscattered signals is considered in this paper. A wavelet packet-based feature extraction scheme is used in conjunction with the linear prediction coding scheme as the front-end-processor. Selected features with higher discriminatory power are then fed to a neural network classifier. Several different classification systems are benchmarked in this paper. These include K-nearest neighbor classifier, PNN and SVM. These paradigms are examined on the acoustic backscattered data for both 40 KHz and 80 KHz sonar bandwidth. Performance comparison of these systems with that of the previously used Back-Propagation Neural Network is provided as well.
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Donghui Li, Mahmood R. Azimi-Sadjadi, and Gerald J. Dobeck "Comparison of different neural network classification paradigms for underwater target discrimination", Proc. SPIE 4038, Detection and Remediation Technologies for Mines and Minelike Targets V, (22 August 2000); https://doi.org/10.1117/12.396261
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