Sonar Foundational Model for Representation Learning and Automatic Target Recognition Systems in Underwater Maritime Environment

水下海洋环境中表示学习和自动目标识别系统的声纳基础模型

• 批准号: 2903803
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2903803
• 关键词: Sonar; Foundational; Model; Representation; Learning

Advances in deep learning have not yet led to a practical revolution in underwater Automatic Target Recognition using passive sonar, in contrast to deep learning's success in other application domains. This is at least in part because the diversity and complexity of sonar data combined with relatively limited datasets has not yet led to deep learning systems that outperform traditional signal processing approaches reliably across a range of underwater environments. This project aims to achieve a breakthrough in passive sonar ATR by developing a foundational model for passive sonar. First, it will aggregate existing sonar datasets to train a more general and robust sonar representation. Secondly, it will study how this representation can support data-efficient learning for ATR, tracking, etc as well as domain generalisation techniques to guarantee generalisation across different oceanic conditions and hydrophone types.

与深度学习在其他应用程序领域的成功相比，深度学习的进步尚未导致水下自动目标识别的实践革命。这至少部分是因为声纳数据的多样性和复杂性以及相对有限的数据集并未导致深度学习系统超过传统信号处理，从而在一系列水下环境中可靠地接近。该项目旨在通过为被动声纳开发基础模型来实现被动声纳ATR的突破。首先，它将汇总现有的声纳数据集，以培训更一般和强大的声纳代表。其次，它将研究该表示形式如何支持ATR，跟踪等的数据有效学习，以及域的概括技术，以确保在不同的海洋条件和水文类型上进行概括。