Task-Oriented Sensing and Covert Transmissions for Collaborative Multi-AUV Systems

arXiv:2607.13880v2 Announce Type: replace Abstract: In underwater covert cooperative missions, autonomous underwater vehicles (AUVs) often cannot rely on active sonar to continuously obtain complete information, since active sensing and frequent communications increase exposure risk. As a result, AUVs primarily rely on passive observation, which yields incomplete local perception and limited task efficiency. Although underwater acoustic communications can mitigate this limitation through information sharing, they are simultaneously constrained by long delays, severe interference, low reliability, and the risk of covert exposure. Existing communications-oriented multi-agent reinforcement learning (MARL) studies often model communication as an ideal information flow, whereas traditional communication optimization primarily focuses on link-level performance. However, both are insufficient to characterize the actual contribution of perceptual information to cooperative tasks under realistic covert physical communication conditions. This paper proposes a Sensed-information Value Realization Multi-Agent Reinforcement Learning (SVR-MARL) framework that leverages practical information to characterize information utility for cooperative tasks and learns distributed cooperative policies under realistic communication and covert constraints. Through a case study of covert multi-AUV cooperative localization and tracking, the potential of the proposed framework to improve collaborative task efficiency while reducing unnecessary communication and exposure risks is demonstrated.