Joint Optimization of Cooperation Efficiency and Communication Covertness for Target Detection with AUVs

2025-10-20

AI Summary

Addressing the challenge of balancing cooperative efficiency and communication stealth in underwater multi-AUV collaborative target detection, this paper proposes a hierarchical action management framework. At the macro level, task allocation is formulated as a centralized Markov Decision Process (MDP); at the micro level, trajectory planning and transmission power control are jointly optimized via a decentralized Partially Observable MDP (POMDP). The framework supports centralized training with decentralized execution, integrating Proximal Policy Optimization (PPO) and Multi-Agent PPO, while incorporating realistic underwater acoustic channel modeling, dynamic energy consumption analysis, and adaptive trajectory design. Experimental results demonstrate that, under strict energy and maneuverability constraints, the method significantly improves both covert communication success rate and target detection accuracy. This provides a deployable solution for intelligent, adversarial underwater multi-agent coordination.

Technology Category

• Multiagent Systems: Coordination and Collaboration
• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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 local observations. Under the centralized training and decentralized execution paradigm, our target detection framework enables adaptive covert cooperation while satisfying both energy and mobility constraints. By comprehensively modeling the considered system, the involved signals and tasks, as well as energy consumption, theoretical insights and practical solutions for the efficient and secure operation of multiple AUVs are provided, offering significant implications for the execution of underwater covert communication tasks.

Problem

• Optimizing AUV trajectory and power for target detection
• Balancing cooperation efficiency with communication covertness
• Developing hierarchical framework for multi-agent underwater coordination

Innovation

• Hierarchical action management framework for optimization
• Macro level Markov decision process for task allocation
• Micro level decentralized decision-making for trajectory control