Underwater acoustic situations are generally considered as an additive white Gaussian noise scenario. The source-specific deterministic features thus extracted should be capable of providing the unique set of classification clues. The source-specific deterministic features capable of disclosing their generating mechanism are extracted from the target emanations received by the hydrophone array. Ocean’s random heterogeneity induces various noises, distortions, and signal-degrading agents that affect the acoustic emanations from the target. The problem of identification of the noise sources in the ocean is of prime importance because of its diverse applications in commercial as well as military sonar applications, which includes detection, underwater monitoring, and classification missions. The ambient acoustic environment of the ocean is complex and includes a variety of noise sources which are of manmade as well as natural in origin. The performance of the classifier in different operating conditions is investigated, with underwater target signals consisting of the real field data collected during expedition, and the results are presented in this paper. The classifier performance has been analyzed in Rayleigh fading conditions, based on which the performance is enhanced by incorporating an autoregressive (AR) Rayleigh fading channel compensation. The robustness of the gammatone cepstral coefficients (GTCC) in combination with the statistical Euclidean distance, artificial neural network (ANN), and hidden Markov model (HMM) classifiers has been investigated, and its performance is compared with that of other feature extraction schemes. The selection of source-specific deterministic features in automatic target recognition (ATR) system is very significant, since it determines the reliability, efficiency, and success rate of the classifier.
Classifiers are often trained with known acoustic target specimens with their characteristic feature set and tested with measurements obtained from the sonar that is deployed in the surveillance or observation zone.
Underwater acoustic target classifiers are found to have many applications in military and security areas where a higher degree of prediction accuracy is needed that makes classifier efficiency and reliability an interesting subject.
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