Autonomous underwater vehicle (AUV) technology has matured sufficiently to allow large areas of seafloor to be covered quickly. This has led to an increased interest in automated mine countermeasures (MCM) techniques. The majority of current models require training, and their success rate is often dependent on the similarity between the testing data and the data used to train the system. They also often produce a black box solution to the problem. Therefore, while the correct result may be obtained, it is often very difficult to determine why the model has produced such a result. The approach detailed in this article is a model-based alternative to traditional supervised models.
|Number of pages||5|
|Publication status||Published - Nov 2004|