Underwater target pose Recognition: A deep learning approach based on sonar signals

Jikai Yang, Ziyan Gu, Peijun Li, Zihan Li, Wei Li,
Underwater target pose Recognition: A deep learning approach based on sonar signals,
Engineering Applications of Artificial Intelligence,
Volume 156, Part B,
2025,
111309,
ISSN 0952-1976,
https://doi.org/10.1016/j.engappai.2025.111309.
(https://www.sciencedirect.com/science/article/pii/S0952197625013119)
Abstract: Underwater target pose recognition has become a significant research focus in ocean exploration, resource investigation, and military applications. Traditional methods based on physical models and rule-based matching struggle with noise interference and dynamic underwater conditions. In this study, we propose an artificial intelligence-based approach, employing a multi-task deep learning model to enhance underwater target pose estimation. A synthetic sonar frequency response dataset was generated by simulating the backscattering characteristics of ellipsoidal targets under various incident angles. A multi-layer neural network was designed to simultaneously perform ellipsoid ratio classification and incidence angle estimation, utilizing a shared feature extraction framework for joint classification and regression learning. Experimental results demonstrate that the proposed model achieves a classification accuracy of 100 % under standard conditions and a mean absolute error (MAE) of 0.0595° in angle estimation. Even under significant noise interference (10 % noise added), the model maintains a classification accuracy of 99.5 % and an MAE of 0.3805°. In extreme conditions with high noise and strong signal attenuation, the model achieves 99 % classification accuracy and an MAE of 0.4328°, demonstrating its robustness and adaptability to complex underwater environments. These findings demonstrate that deep learning serves as a robust alternative to traditional physics-based modeling, significantly enhancing the precision and reliability of underwater target recognition. Future research will integrate real-world sonar data and explore advanced AI architectures such as convolutional neural networks (CNNs) and Transformers for enhanced feature extraction and generalization.
Keywords: Underwater target recognition; Pose estimation; Deep learning applications; Multi-layer neural networks; Sonar signal processing; Multi-task learning