  Swarms of drones offer an increased sensing aperture, and having them mimic
behaviors of natural swarms enhances sampling by adapting the aperture to local
conditions. We demonstrate that such an approach makes detecting and tracking
heavily occluded targets practically feasible. While object classification
applied to conventional aerial images generalizes poorly the randomness of
occlusion and is therefore inefficient even under lightly occluded conditions,
anomaly detection applied to synthetic aperture integral images is robust for
dense vegetation, such as forests, and is independent of pre-trained classes.
Our autonomous swarm searches the environment for occurrences of the unknown or
unexpected, tracking them while continuously adapting its sampling pattern to
optimize for local viewing conditions. In our real-life field experiments with
a swarm of six drones, we achieved an average positional accuracy of 0.39 m
with an average precision of 93.2% and an average recall of 95.9%. Here,
adapted particle swarm optimization considers detection confidences and
predicted target appearance. We show that sensor noise can effectively be
included in the synthetic aperture image integration process, removing the need
for a computationally costly optimization of high-dimensional parameter spaces.
Finally, we present a complete hard- and software framework that supports
low-latency transmission (approx. 80 ms round-trip time) and fast processing
(approx. 600 ms per formation step) of extensive (70-120 Mbit/s) video and
telemetry data, and swarm control for swarms of up to ten drones.
