Marine Multi-Physics-Based Hierarchical Fusion Recognition Method for Underwater Targets

With the rapid advancement of ocean monitoring technology, the types and quantities of underwater sensors have increased significantly. Traditional single-sensor approaches exhibit limitations in underwater target classification, resulting in low classification accuracy and poor robustness. This paper integrates deep learning and information fusion theory to propose a multi-level fusion perception method for underwater targets based on multi-physical-field sensing. We extract both conventional typical features and deep features derived from an autoencoder and perform feature-level fusion. Neural network-based classification models are constructed for each physical field subsystem. To address the class imbalance and difficulty imbalance issues in the collected physical field target samples, we design a C-Focal Loss function specifically for the three underwater target categories. Furthermore, based on the confusion matrix results from the subsystem’s validation set, we propose a neural network-based Dempster–Shafer evidence fusion method (NNDS). Experimental validation using real-world data demonstrates a 97.15% fusion classification accuracy, significantly outperforming both direct multi-physical-field network fusion and direct subsystem decision fusion. The proposed method also exhibits superior reliability and robustness.