Performance improvement of active sonar target classification using feature fusion

In this study, we propose a neural network architecture considering feature fusion to improve active sonar target classification, where training data is scarce. We use various hand-crafted features with different perspectives for the active sonar one- and two-dimention raw data to enhance target classification performance. We use two kinds of features, in 1D feature and 2D features, because different perspectives on the same sample complement effectively by exchanging. For this, we contrived attention based complementary learning module to extract comprehensive features using the two kinds of features. To verify the generalization performance of this model, two real-ocean datasets were used and the classification results are compared with existing DL models.