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This article presents the implementation of a multithreaded parallel architecture, which enables telescope-based optical unmanned aerial vehicle (UAV) detection and tracking in real time. For efficient image processing an accurate deep learning object detector is complemented in parallel by a fast object tracker. A transition strategy between detector and tracker is introduced based on the tracker reliability, which improves the object localization accuracy of the system. The deep learning algorithm initializes the tracker and in the subsequent frames the reliability of the tracker is compared to the confidence value of each newly detected object to determine whether a reinitialization is necessary. The implemented architecture successfully demonstrates the parallel combination of an FRCNN detector and a MEDIANFLOW tracker to achieve visual UAV detection and tracking at 100 fps. The proposed reliability-based strategy outperforms a purely detector and tracker-based strategy by 6% and 14%, respectively, in terms of intersection over union at a threshold of 0.5, in scenarios, when the target UAV is flying in front of a complex background. In addition, the implemented parallel architecture increases the probability for a flight path estimation, which requires at least two localizations, by 49%, when compared to a nonparallel architecture. Field tests are conducted with the proposed architecture using a telescope system demonstrating UAV detection and tracking at 100 fps in distances up to 4000 m in front of a clear background.

As commercially available CubeSats with up to six standardized units cannot achieve the precision required for an instantaneous establishment of a low-divergence optical inter-satellite link, search patterns are used to scan the remaining field of uncertainty. This analysis optimizes the simultaneously executed search pattern combinations of the two laser communication terminals involved. Based on a Monte Carlo simulation, the perturbations on these links are investigated, and the corresponding key performance parameters such as mean acquisition time and success rate are calculated. The results are penalized by the hardware specifications, including actuator and sensor bandwidths, given by their design. Residual attitude error components imply a significant influence on the acquisition process and are therefore presented within this work. The pattern pairs are fed through an automated optimization algorithm to tune and analyze them. In this particular scenario of two CubeISL models, the mean duration for a first detected acquisition hit is within a pattern period of 3.2 s for the best performing pairs spiral-rose and lissajous-rose. Assuming an uncertainty field of ${\pm}0.2\;{\rm deg}$ due to limited attitude knowledge, success rates between 82.3% and 99.9% are achieved.

Recent advancements in free-space optical (FSO) communications are enabling a breakthrough in satellite miniaturization and data transfer rates. The CubeISL laser communication terminal (LCT), a development of the German Aerospace Center (DLR), is set to showcase inter-satellite links at 100 Mbps and downlinks at 1 Gbps once it launches in 2025. This technology aims to establish itself as the cutting-edge solution for efficient CubeSat communications, delivering high data rates. To validate its capabilities, the terminal underwent rigorous testing in a 143 km FSO link between the astronomical sites of La Palma and Tenerife in the Canary Islands. The European Space Agency’s Optical Ground Station emulated downlinks, while the communication between two LCTs simulated inter-satellite links. This paper outlines the current developmental stage of the CubeISL LCT and presents the outcomes of its horizontal link demonstration.