This work contributes to efforts on autonomously detecting a vegetation-occluded target by airborne observers. It investigates and enhances previous work on a Particle Swarm Optimization (PSO) strategy for Airborne Optical Sectioning (AOS) drone swarms. First, it identifies two issues with that method and proposes to resolve them by a leader stabilization for its scattering and projection-based line positions for its default scanning pattern. Second, it connects this method to other PSO variants and presents a new adaptive PSO strategy for AOS drone swarms that draws on the ideas of Adaptive PSO (APSO).

翻译：本研究致力于通过空中观测平台自主检测植被遮挡目标。研究探讨并改进了此前针对空中光学切片（AOS）无人机群提出的粒子群优化（PSO）策略。首先，识别出该方法存在的两个问题，并提出通过引入引导者稳定性机制（解决其散射问题）及基于投影线的位置调整策略（修正默认扫描模式）予以解决。其次，将该方法与其他PSO变体进行关联，融合自适应PSO（APSO）思想，提出一种适用于AOS无人机群的新型自适应PSO策略。