存在空间障碍物和禁区时的鲁棒传感器覆盖
Robust sensor coverage in the presence of spatial obstacles and exclusion zones
浏览论文内容
中文总结 AI 辅助
针对位置不确定的障碍物感知空中定向传感器网络，开发鲁棒优化框架，用基于扇区传感模型和统一几何公式建模。提出三种策略，经鲁棒方向优化与目标感知睡眠调度，模拟表明该框架鲁棒节能，性能优于文献方法。
中文摘要 AI 辅助
在本文中，我们针对在位置不确定情况下运行的障碍物感知空中定向传感器网络，使用基于扇区的传感模型开发了一个鲁棒优化框架。将监测区域离散化为网格点，并开发了一个统一的几何公式来对定向传感进行建模，同时明确考虑障碍物引起的能见度损失和禁区。为了在不确定性下提高传感性能，提出了三种渐进优化策略，即鲁棒空中网格覆盖（RAGC）、鲁棒空中目标覆盖（RATC）和鲁棒空中目标调度（RATS）。该框架基于鲁棒可行性半径进行鲁棒方向优化，以在传感器扰动下最大化有效网格和目标覆盖，随后是一种目标感知睡眠调度机制，在不影响目标监测的情况下最小化活动传感器的数量。在不同目标分布、障碍物配置、不确定性水平和传感器故障场景下的大量模拟表明，所提出的框架实现了鲁棒且节能的传感，同时在覆盖质量、目标监测和传感器利用率方面始终优于文献中的代表性方法。
英文摘要
In this article, we develop a robust optimization framework for obstacle-aware aerial directional sensor networks operating under positional uncertainty using a sector-based sensing model. The monitoring area is discretized into grid points and a unified geometric formulation is developed to model directional sensing while explicitly accounting for obstacle-induced visibility loss and exclusion regions. To enhance sensing performance under uncertainty, three progressive optimization strategies, namely robust aerial grid coverage (RAGC), robust aerial target coverage (RATC) and robust aerial target scheduling (RATS), are proposed. The framework employs robust orientation optimization based on the radius of robust feasibility to maximize effective grid and target coverage under sensor perturbations, followed by a target-aware sleep scheduling mechanism that minimizes the number of active sensors without compromising target monitoring. Extensive simulations under varying target distributions, obstacle configurations, uncertainty levels and sensor failure scenarios demonstrate that the proposed framework achieves robust and energy-efficient sensing while consistently outperforming representative approaches from the literature in terms of coverage quality, target monitoring and sensor utilization.
