A comparison of different machine learning algorithms for automatic classification of sonar targets

A well-known problem with modern anti-submarine warfare sonars with narrow beamwidths and wide frequency bandwidths, is the frequent occurence of false alarms, particularly in littoral environments. This increases the workload of sonar operators and also reduces the usefulness of automatic systems such as autonomous underwater vehicles, since their limited communication abilities hinder them from sharing large amounts of contacts. In this paper, four traditional machine learning algorithms are tested on sonar data with a high amount of false alarms together with synthetic submarine echoes. It is shown that some of the algorithms can outperform simple signal to noise ratio (SNR) thresholding by a significant amount, but that the performance is highly dependent on the parameter values chosen for each algorithm. These parameters are therefore investigated in order to determine their relative significance.