Waveguide systems are usually matched by matching terminations. These terminations will absorb all the incident power and there should be no reflection from the termination. Similarly when components are added to a waveguide system they are usually matched as far as possible. Hence in properly matched systems the reflected wave should be zero. However, it may happen that there is a small reflected wave which gives rise to a standing wave in the guide system. This is usually measured by a standing wave detector with crystal assembly and meter. If the matching is very nearly perfect the reflected wave is so small that the standing wave ratio is of the order of 1·01 or much less. · Under such circumstances the method described below becomes very useful.In this method a short piece of waveguide containing a small probe is introduced into the system. This probe causes a reflected wave which will augment the reflection from the termination. Hence the standing wave becomes very large and it can be easily measured by the standing wave detector. From this the small reflection of the termination or mismatch can be easily calculated.

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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. 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. 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. 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 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 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.

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