Adaptive Particle Swarm Optimization for through-foliage target detection with drone swarms
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).
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Synthetic Aperture Sensing for Occlusion Removal with Drone Swarms
We demonstrate how efficient autonomous drone swarms can be in detecting and tracking occluded targets in densely forested areas, such as lost people during search and rescue missions. Exploration and optimization of loc…
A theoretical guideline for designing an effective adaptive particle swarm
In this paper we theoretically investigate underlying assumptions that have been used for designing adaptive particle swarm optimization algorithms in the past years. We relate these assumptions to the movement patterns …
Color Image Segmentation using Adaptive Particle Swarm Optimization and Fuzzy C-means
Segmentation partitions an image into different regions containing pixels with similar attributes. A standard non-contextual variant of Fuzzy C-means clustering algorithm (FCM), considering its simplicity is generally us…
ClusteringEvolutionary AlgorithmsImage SegmentationSegmentation+1A Novel adaptive optimization of Dual-Tree Complex Wavelet Transform for Medical Image Fusion
In recent years, many research achievements are made in the medical image fusion field. Fusion is basically extraction of best of inputs and conveying it to the output. Medical Image fusion means that several of various …
SSIMParameter Adaptation and Criticality in Particle Swarm Optimization
Generality is one of the main advantages of heuristic algorithms, as such, multiple parameters are exposed to the user with the objective of allowing them to shape the algorithms to their specific needs. Parameter select…