Enabling Cooperative Autonomy in UUV Clusters: A Survey of Robust State Estimation and Information Fusion Techniques

Highlights: What are the main findings? Our quantitative analysis reveals a fundamental trade-off in cooperative navigation: batch optimization methods like FGO provide the highest accuracy at a significant computational cost, while robust filters like MCC-KF offer resilience to non-Gaussian noise with much greater efficiency but are prone to long-term drift. A critical review of intelligent strategies (e.g., DRL, Semantic Communication) and advanced navigation techniques shows that while conceptually powerful, their practical deployment is currently hindered by specific, unresolved challenges such as the sim-to-real gap, hyperparameter instability, and the lack of standardized underwater datasets. What is the implication of the main finding? The key implication is that the optimal algorithm choice is a mission-specific engineering decision, not a one-size-fits-all solution; this paper provides the first data-driven framework (via our quantitative comparison) to guide researchers and engineers in making this critical trade-off between accuracy and resource constraints. This work implies that future research should prioritize the specific, practical bottlenecks identified in this review, and our data-driven roadmap provides concrete, actionable research questions to accelerate the transition of these advanced technologies from theory to robust, real-world application in autonomous underwater systems. Cooperative navigation is a fundamental enabling technology for unlocking the full potential of Unmanned Underwater Vehicle (UUV) clusters in GNSS-denied environments. However, the severe constraints of the underwater acoustic channel, such as high latency, low bandwidth, and non-Gaussian noise, pose significant challenges to designing robust and efficient state estimation and information fusion algorithms. While numerous surveys have cataloged the available techniques, they have remained largely descriptive, lacking a rigorous, quantitative comparison of their performance trade-offs under realistic conditions. This paper provides a comprehensive and critical review that moves beyond qualitative descriptions to establish a novel quantitative comparison framework. Through a standardized benchmark scenario, we provide the first data-driven, comparative analysis of key frontier algorithms—from recursive filters like the Maximum Correntropy Kalman Filter (MCC-KF) to batch optimization methods like Factor Graph Optimization (FGO)—evaluating them across critical metrics including accuracy, computational complexity, communication load, and robustness. Our results empirically reveal the fundamental performance gaps and trade-offs, offering actionable insights for system design. Furthermore, this paper provides in-depth technical analyses of advanced topics, including distributed fusion architectures, intelligent strategies like Deep Reinforcement Learning (DRL), and the unique challenges of navigating in extreme environments such as the polar regions. Finally, leveraging the insights derived from our quantitative analysis, we propose a structured, data-driven research roadmap to systematically guide future investigations in this critical domain.