Underwater acoustic target recognition has always played a pivotal role in ocean remote sensing. By analyzing and processing ship-radiated signals, it is possible to determine the type and nature of a target. Historically, traditional signal processing techniques have been employed for target recognition in underwater environments, which often exhibit limitations in accuracy and efficiency. In response to these limitations, the integration of artificial intelligence (AI) methods, particularly those leveraging machine learning and deep learning, has attracted increasing attention in recent years. Compared to traditional methods, these intelligent recognition techniques can autonomously, efficiently, and accurately identify underwater targets. This paper comprehensively reviews the contributions of intelligent techniques in underwater acoustic target recognition and outlines potential future directions, offering a forward-looking perspective on how ongoing advancements in AI can further revolutionize underwater acoustic target recognition in ocean remote sensing.

Deep learning has made data-driven methods effective in underwater acoustic target recognition (UATR) using passive sonar signals. However, a major current challenge is the limited availability of underwater acoustic data, leading to suboptimal performance without sufficient data. Self-supervised learning (SSL) can help address this problem by learning intrinsic patterns within acoustic data. Nonetheless, applying SSL in UATR systems requires efficient learning of meaningful representations that can provide quick prediction speed for real-time recognition systems. To this end, we propose the masking hierarchical tokens (MHT) method to learn meaningful representations via efficient self-supervised learning for our previously proposed UATR-Transformer, giving rise to the MHT-UATR-Transformer. In particular, the MHT-UATR-Transformer first exploits a new designed token-convolution-based hierarchical tokenization to efficiently obtain rich time–frequency information from the input Mel-spectrogram. Then, most of these tokens are masked with a high masking ratio and subsequently reconstructed by an integrated Encoder–Decoder structure. In this way, the MHT-UATR-Transformer can learn intrinsic representations of underwater acoustic signals to achieve better recognition performance with fewer labeled data, which is expected to alleviate the dependency on expensive acoustic data. Experimental results on two widely studied underwater databases show that our proposed method achieves better performance than supervised learning and state-of-the-art SSL method in both accuracy and speed, especially in few-shot and noisy scenarios, thus enhancing its practicality in real marine applications.

Deep learning models can produce unstable results by introducing imperceptible perturbations that are difficult for humans to recognize. This can have a significant impact on the accuracy and security of deep learning applications due to their poorly understood interpretability. As a field critical to security research, this problem clearly exists in underwater acoustic target recognition for ocean sensing. To address this issue, this article investigates the reliability of state-of-the-art deep learning models by exploring adversarial attack methods that add small, exquisite perturbations on acoustic Mel-spectrograms to generate adversarial spectrograms. Experimental results based on real-world datasets reveal that these models can be forced to learn unexpected features when subjected to adversarial spectrograms, resulting in significant accuracy drops. Specifically, when employing the iterative attack method, the overall accuracy of all models experiences a significant decrease of approximately 70% for two datasets under stronger perturbations.