Comparison of classification accuracy and time of support vector machine and neural network algorithms for diver detection

Unique features and the possibility of easy dissemination of acoustic signals in underwater environment provide the ability to identify and track the underwater targets. From applications of acoustic signals in passive defense, the following can be named: use of sonar for diver detection in order to prevent the diver’s entrance to harbor equipment plants, protection from the coastal power and so on….for this goal, correct diver detection from other underwater targets such as: dolphins and fish schools is important. In this paper, for diver detection, two classification algorithms, support vector machine (SVM) and neural network, have been used. For this work, two features, target strength (TS) and speed have been used. Finally the results of these algorithms have been evaluated based on classification and detection accuracy and time. The simulations indicate that, MLP neural network algorithm has the best result.