Extraction of comprehensive feature for active target detection using two hand-crafted acoustic features

We propose a model for creating a new feature suitable for classifying targets and non-targets using a small active sonar data. The proposed model extracts a comprehensive feature from two different hand-crafted features using deep-learning (DL). To extract comprehensive feature, we contrived a complementary learning module (CLM), which consists of two stages. In the first stage, a transformer encoder was adopted to reinforce the input features and then complementarily learned through attention mechanism in the following stage. The output features from the CLM are passed through a shallow CNN to classify targets and non-targets. In addition, an uncertainty quantification method is proposed using two CLMs to quickly add new samples acquired in the real ocean dataset. Using the decisions from two CLMs, we calculate the variance and determine which samples from the entire dataset are worthy of prioritized addition. Two real-ocean datasets were used to examine the model generalization and compare the classification results with conventional DL models. The generalization performance of the proposed model was comparable or superior to other DL models and uncertainty quantificaion method improved the generalization performance. [Work supported by Korea Research Institute for defense Technology planning and advancement(KRIT)—Grant funded by the Korea government (DAPA—Defense Acquisition Program Administration) (No. KRIT-CT-23-026, Integrated Underwater Surveillance Research Center for Adapting Future Technologies, 2024)]