Multi-UAV Cooperative Navigation Based on Multisource Information Fusion: A Review

Unmanned aerial vehicle (UAV) swarms are increasingly deployed in both civilian and military domains, where high-precision navigation in GNSS-denied environments remains a critical challenge. Cooperative navigation has emerged as a promising paradigm by leveraging inter-vehicle information exchange and multi-source information fusion to enhance swarm navigation performance. Although numerous surveys have summarized multi-sensor integration and state estimation methods for single-platform UAVs, comprehensive reviews dedicated to cooperative navigation remain limited. This paper presents a structured framework for cooperative navigation that integrates multi-source information acquisition, cooperative localization techniques in GNSS-denied environments, and multi-source fusion algorithms. On this basis, we provide a systematic review of cooperative navigation from three perspectives: relative measurement information, cooperative navigation architectures, and swarm cooperative optimization algorithms. Furthermore, fusion algorithms are categorized into four classes, namely mathematically derived, graph-based, filtering-based, and learning-based approaches, with their development trends systematically analyzed. The synergistic features of multi-source information are then discussed, with emphasis on information selection, fault tolerance, and resilient fusion. Finally, the hierarchical structure of multi-source information fusion is highlighted, revealing the evolutionary trend from feature-level toward decision-level strategies. This survey aims to present a holistic understanding of UAV cooperative navigation, outline key technical challenges, and shed light on future research directions in this rapidly evolving field.