Synthetic Aperture Sensing for Occlusion Removal with Drone Swarms

TL;DR

This paper presents a drone swarm-based synthetic aperture sensing method for efficient occlusion removal and target detection in dense forests, outperforming traditional blind sampling strategies.

Contribution

It introduces a real-time particle swarm optimization with a novel objective function tailored for dynamic foliage conditions, advancing autonomous occlusion removal techniques.

Findings

Faster and more reliable target detection in forested environments.

Effective handling of highly variable occlusion conditions.

Enhanced sampling efficiency through synthetic aperture principles.

Abstract

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 local viewing conditions, such as occlusion density and target view obliqueness, provide much faster and much more reliable results than previous, blind sampling strategies that are based on pre-defined waypoints. An adapted real-time particle swarm optimization and a new objective function are presented that are able to deal with dynamic and highly random through-foliage conditions. Synthetic aperture sensing is our fundamental sampling principle, and drone swarms are employed to approximate the optical signals of extremely wide and adaptable airborne lenses.