Underwater acoustic target classification using Fuzzy Inference System with Ensemble Learning

Due to the complexity of the marine environment, underwater signals exhibit highly nonlinear characteristics, making underwater acoustic target recognition extremely challenging. Existing methods not only require further improvement in recognition accuracy but also lack interpretability. To address these issues, this paper proposes a novel underwater acoustic target recognition method called the Underwater Acoustic Fuzzy Inference System with ensemble learning (UAFISEL). UAFISEL employs a zero-order fuzzy inference system (ZOFIS) to model nonlinear underwater signals and further boosts the recognition capability through ensemble learning. UAFISEL possesses the following characteristics: (1) It is capable of modeling nonlinear signals from underwater acoustic targets (UAT). (2)It adopts ZOFIS as the base learner (ensemble component) to ensemble a multi-class classifier. (3)It possesses strong generalization capabilities, and works well on small sample datasets. (4)It has excellent interpretability due to the rules of ZOFIS. Experimental results obtained from two publicly-accessible underwater acoustic databases exhibit the capability of UAFISEL to surpass other leading algorithms, encompassing both traditional statistics-based and state-of-the-art learning-based methods. UAFISEL demonstrates a significant performance edge in few-shot learning environments, and possesses enhanced interpretability of its results.