UAWC: An intelligent underwater acoustic target recognition system for working conditions mismatching

Underwater acoustic target recognition (UATR) systems are crucial to both military and civilian activities. However, the complex ship working conditions will largely affect the performance of recognition systems, especially in the case of working conditions mismatching (WCMM). For WCMM problems, an intelligent UATR system for working condition mismatching (UAWC) is proposed. UAWC uses auditory features as input to the system and uses knowledge distillation to learn the intrinsic connections of target features under different working conditions. In the proposed approach, the teacher network obtains initial knowledge by utilizing a large amount of existing working condition data. Next, the student network uses a small amount of target working data for training, and extracts incremental knowledge in teacher network through knowledge distillation technology to enhance the accuracy of its classification of target working data, so as to effectively deal with the WCMM problem.The tests make use of datasets for ship-radiated noise under various working conditions.The results showed that UAWC performs better than other methods on a wide range of WCMM problems.