智能无人水下航行器(AUV)的自适应智能发展对水下关联环境的准确感知提出了迫切需求。动态场景中运动目标理解是AUV环境感知的基础支撑技术，可为路径规划、态势评估、危险规避等自主行为提供关键技术保障。本项目围绕AUV自主能力发展需求，以提升AUV水下环境感知能力为目标，开展大尺度海洋环境下基于AUV声视觉的多动态目标跟踪理论和关键技术研究：构建融合复杂海洋环境、平台运动特性以及运动目标先验形状表达和估计为一体的目标分割理论模型；改进基于多特征描述的多水下动态目标跟踪方法；研究基于似物性采样和卷积神经网络的深度学习跟踪方法；提出面向多线索学习的水下运动目标协同跟踪方法。希望通过本项目的实施，解决水下背景噪声干扰下的多运动目标持续跟踪问题，实现传统水声探测技术与新兴人工智能技术、强烈工程发展需求牵引下的关键技术研究与系统性基础理论研究的结合，推动AUV水下感知技术与应用研究取得进步。

For the adaptive autonomy of AUV, it is an urgent need to detect the underwater environments accurately. As the basic supporting technology of AUV environment perception, the ability of moving targets detection in dynamic scenes can provide the key technical support for AUV autonomous behaviours, such as path planning, situation assessment and objects avoidance. To focus on the development of autonomous capabilities and aim to improve the perception of underwater environment, the project will centre on the multi-dynamic targets robust tracking theory and key technologies of AUV in large-scale marine environment: The target segmentation theory model is constructed, which the complex marine environment, the platform motion characteristics and the priori shape representation are integrated in.The multi-underwater dynamic target tracking method based on multi-feature description is improved. The deep learning tracking method based on analogue-like sampling and convolutional neural network is studied. A collaborative tracking method of underwater moving target based on multi-cue learning is proposed. It is expected the problem of the continuous tracking of multiple moving targets under the interference of underwater background noise will be solved. In the project, the traditional underwater acoustic detection technology and emerging artificial intelligence technology will be realized, and the key technology research are combined with systematic basic theory perfectly, the achievement will promote the AUV underwater perception technology and application of research progress.