In recent years, autonomous underwater vehicle (AUV) systems have demonstrated significant potential in complex marine exploration. However, effective AUV-based tracking remains challenging in realistic underwater environments characterized by high-dimensional features, including coupled kinematic states, spatial constraints, time-varying environmental disturbances, etc. To address these challenges, this paper proposes a hierarchical embodied-intelligence (EI) architecture for underwater multi-target tracking with AUVs in complex underwater environments. Built upon this architecture, we introduce the Double-Head Encoder-Attention-based Multi-Expert Collaborative Decision (DHEA-MECD), a novel Deep Reinforcement Learning (DRL) algorithm designed to support efficient and robust multi-target tracking. Specifically, in DHEA-MECD, a Double-Head Encoder-Attention-based information extraction framework is designed to semantically decompose raw sensory observations and explicitly model complex dependencies among heterogeneous features, including spatial configurations, kinematic states, structural constraints, and stochastic perturbations. On this basis, a motion-stage-aware multi-expert collaborative decision mechanism with Top-k expert selection strategy is introduced to support stage-adaptive decision-making. Furthermore, we propose the DHEA-MECD-based underwater multitarget tracking algorithm to enable AUV smart, stable, and anti-interference multi-target tracking. Extensive experimental results demonstrate that the proposed approach achieves superior tracking success rates, faster convergence, and improved motion optimality compared with mainstream DRL-based methods, particularly in complex and disturbance-rich marine environments.

翻译：近年来，自主水下航行器（AUV）系统在复杂海洋探测中展现出巨大潜力。然而，在具有高维特征（包括耦合的运动学状态、空间约束、时变环境扰动等）的真实水下环境中，实现有效的基于AUV的跟踪仍然具有挑战性。为应对这些挑战，本文提出了一种用于复杂水下环境中AUV多目标跟踪的分层具身智能（EI）架构。基于此架构，我们提出了基于双头编码器-注意力的多专家协作决策（DHEA-MECD），这是一种新颖的深度强化学习（DRL）算法，旨在支持高效、鲁棒的多目标跟踪。具体而言，在DHEA-MECD中，设计了一个基于双头编码器-注意力的信息提取框架，用于对原始感官观测进行语义分解，并显式建模异构特征（包括空间构型、运动学状态、结构约束和随机扰动）之间的复杂依赖关系。在此基础上，引入了一种采用Top-k专家选择策略的运动阶段感知多专家协作决策机制，以支持阶段自适应的决策。此外，我们提出了基于DHEA-MECD的水下多目标跟踪算法，以实现AUV智能、稳定且抗干扰的多目标跟踪。大量实验结果表明，与主流的基于DRL的方法相比，所提方法在跟踪成功率、收敛速度和运动最优性方面均表现更优，尤其是在复杂且干扰丰富的海洋环境中。