面向航空传感器网络的鲁棒性感知优先级覆盖优化
Robust priority-aware coverage optimization for aerial sensor networks
AI总结：
针对航空传感器网络的感知器位置不确定性问题，提出优先级感知鲁棒覆盖优化框架与PAROO算法，可将传感资源导向高优先级区域，提升加权覆盖度，适用于安全敏感环境。
AI中文摘要：
本文提出一种航空传感器网络下的感知器位置不确定性感知优先级鲁棒覆盖优化框架。每个区域被赋予一个优先级权重，目标是最大化加权覆盖度，同时保持对位置扰动的鲁棒性。研究人员将监视约束和基于RRF的鲁棒性公式纳入所提框架，构建了数学优化模型，随后提出高效的优先级感知鲁棒定向优化（PAROO）算法，以确定能最大化加权覆盖目标的传感器定向。在机场启发式监视场景上的实验结果表明，所提框架能有效将传感资源导向高优先级区域，且比代表性基线方法实现更高的加权覆盖度，凸显其在安全敏感环境中的实用适用性。
英文摘要：
This article presents a priority-aware robust coverage optimization framework for an aerial sensor network under sensor location uncertainty. Each region is assigned a priority weight, and the objective is to maximize the weighted coverage while maintaining robustness against positional perturbations. A mathematical optimization model is developed by incorporating surveillance constraints and an RRF-based robustness formulation into the proposed framework. An efficient priority-aware robust orientation optimization (PAROO) algorithm is then proposed to determine the sensor orientations that maximize the weighted coverage objective. Experimental results on an airport-inspired surveillance scenario demonstrate that the proposed framework effectively directs sensing resources toward high-priority regions and achieves higher weighted coverage than representative baseline approaches, highlighting its practical applicability in security-sensitive environments.
