通过多任务对抗学习，提升水下声学目标识别在复杂环境下的鲁棒性。
Adversarial multi-task underwater acoustic target recognition: towards robustness against various influential factors
- 设计辅助任务建模距离、水深、风速等影响因素
- 在ShipsEar数据集上实现12类识别的领先性能
- 适合关注水下目标识别鲁棒性的研究人员
基于被动声纳的水下声学目标识别在实际海事应用中面临诸多挑战。信号特征易受多种环境条件和数据采集配置影响，导致识别系统不稳定。尽管其他水声领域已针对此类因素开展研究，但水下目标识别领域仍常忽视此问题。本文基于已有标注信息，设计用于建模源距离、水柱深度、风速等影响因素的辅助任务，采用多任务框架将这些因素与识别任务关联。进一步在多任务框架中引入对抗学习机制，促使模型提取对影响因素具有鲁棒性的特征表示。在ShipsEar数据集上的大量实验与分析表明，所提出的对抗多任务模型能有效建模各类影响因素，并在12类识别任务中达到当前最优性能。
原文摘要 · Abstract (English)
Underwater acoustic target recognition based on passive sonar faces numerous challenges in practical maritime applications. One of the main challenges lies in the susceptibility of signal characteristics to diverse environmental conditions and data acquisition configurations, which can lead to instability in recognition systems. While significant efforts have been dedicated to addressing these influential factors in other domains of underwater acoustics, they are often neglected in the field of underwater acoustic target recognition. To overcome this limitation, this study designs auxiliary tasks that model influential factors (e.g., source range, water column depth, or wind speed) based on available annotations and adopts a multi-task framework to connect these factors to the recognition task. Furthermore, we integrate an adversarial learning mechanism into the multi-task framework to prompt the model to extract representations that are robust against influential factors. Through extensive experiments and analyses on the ShipsEar dataset, our proposed adversarial multi-task model demonstrates its capacity to effectively model the influential factors and achieve state-of-the-art performance on the 12-class recognition task.
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