Cooperative Target Tracking of Unmanned Underwater Vehicles: Methods, Challenges, and Future Directions

Abstract

Cooperative target tracking has become a fundamental capability for unmanned underwater vehicles (UUVs) in applications such as maritime surveillance, environmental monitoring, underwater infrastructure inspection, and defense operations. Compared with single-platform tracking, cooperative target tracking enables UUVs to achieve enhanced perception accuracy, wider spatial coverage, improved robustness, and persistent observation through information sharing and coordinated decision-making. However, the unique characteristics of underwater environments, including limited-bandwidth acoustic communication, long transmission delays, intermittent connectivity, environmental disturbances, and constrained platform maneuverability, introduce significant challenges to target state estimation, cooperative control, mission coordination, and system deployment. This paper presents a comprehensive review of recent advances in cooperative target tracking for UUVs. First, the overall architecture and key enabling technologies are introduced. Subsequently, recent progress in target state estimation, collaborative decision-making, and cooperative control is systematically reviewed, with particular emphasis on communication-constrained information fusion, distributed coordination, and learning-based intelligent decision-making. Emerging technologies, including digital twins, embodied intelligence, communication-aware artificial intelligence, and large-scale heterogeneous swarm collaboration, are further discussed from both algorithmic and engineering perspectives. Finally, current research challenges and emerging technologies are summarized to provide insights for future development.