Joint Optimization of Cooperation Efficiency and Communication Covertness for Target Detection with AUVs
Xueyao Zhang, Bo Yang, Zhiwen Yu, Xuelin Cao, Wei Xiang, Bin Guo, Liang Wang, Billy Pik Lik Lau, George C. Alexandropoulos, Jun Luo, M\'erouane Debbah, Zhu Han, Chau Yuen

TL;DR
This paper presents a hierarchical optimization framework for AUVs to enhance cooperative target detection while maintaining communication covertness, balancing efficiency, energy use, and stealth in underwater environments.
Contribution
It introduces a novel hierarchical decision-making approach combining macro-level task allocation with micro-level trajectory and power control for covert underwater operations.
Findings
Effective joint trajectory and power control improves detection success.
Hierarchical framework enables adaptive covert cooperation.
The approach balances energy, mobility, and covertness constraints.
Abstract
This paper investigates underwater cooperative target detection using autonomous underwater vehicles (AUVs), with a focus on the critical trade-off between cooperation efficiency and communication covertness. To tackle this challenge, we first formulate a joint trajectory and power control optimization problem, and then present an innovative hierarchical action management framework to solve it. According to the hierarchical formulation, at the macro level, the master AUV models the agent selection process as a Markov decision process and deploys the proximal policy optimization algorithm for strategic task allocation. At the micro level, each selected agent's decentralized decision-making is modeled as a partially observable Markov decision process, and a multi-agent proximal policy optimization algorithm is used to dynamically adjust its trajectory and transmission power based on its…
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Taxonomy
TopicsUnderwater Vehicles and Communication Systems · Distributed Control Multi-Agent Systems · UAV Applications and Optimization
