Drone swarm strategy for the detection and tracking of occluded targets in complex environments

Abstract

Drone swarms can achieve tasks via collaboration that are impossible for single drones alone. Synthetic aperture (SA) sensing is a signal processing technique that takes measurements from limited size sensors and computationally combines the data to mimic sensor apertures of much greater widths. Here we use SA sensing and propose an adaptive real-time particle swarm optimization (PSO) strategy for autonomous drone swarms to detect and track occluded targets in densely forested areas. Simulation results show that our approach achieved a maximum target visibility of 72% within 14 seconds. In comparison, blind sampling strategies resulted in only 51% visibility after 75 seconds and 19% visibility in 3 seconds for sequential brute force sampling and parallel sampling respectively. Our approach provides fast and reliable detection of occluded targets, and demonstrates the feasibility and efficiency of using swarm drones for search and rescue in areas that are not easily accessed by humans, such as forests and disaster sites.

Fusion of Single and Integral Multispectral Aerial Images

“…These include search and rescue, wildfire detection, wildlife observation, surveillance, forestry, agriculture, and archaeology. With Airborne Optical Sectioning (AOS) [1][2][3][4][5], we have introduced a synthetic aperture imaging technique that removes occlusion in aerial images in real time (Figure 1a-d). It computationally registers and integrates multiple (single) images captured with conventional camera optics at different drone positions into an integral image that mimics a wide (several meters) synthetic aperture camera.…”