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Nanosatellites are being widely used in various missions, including remote sensing applications. However, the difficulty lies in mission operation due to downlink speed limitation in nanosatellites. Considering the global cloud fraction of 67%, retrieving clear images through the limited downlink capacity becomes a larger issue. In order to solve this problem, we propose an image prioritization method based on cloud coverage using CNN. The CNN is designed to be lightweight and to be able to prioritize RGB images for nanosatellite application. As previous CNNs are too heavy for onboard processing, new strategies are introduced to lighten the network. The input size is reduced, and patch decomposition is implemented for reduced memory usage. Replication padding is applied on the first block to suppress border ambiguity in the patches. The depth of the network is reduced for small input size adaptation, and the number of kernels is reduced to decrease the total number of parameters. Lastly, a multi-stream architecture is implemented to suppress the network from optimizing on color features. As a result, the number of parameters was reduced down to 0.4%, and the inference time was reduced down to 4.3% of the original network while maintaining approximately 70% precision. We expect that the proposed method will enhance the downlink capability of clear images in nanosatellites by 112%.

The second-generation star tracker estimates pointing knowledge of a satellite without a-priori knowledge. But star trackers are larger in size, heavier, power hungry and expensive for nanosatellite missions. The Arcsecond Pico Star Tracker (APST) is designed based on the limitations of nanosatellites and estimated to provide pointing knowledge in an arcsecond. The APST will be used on the SNUSAT-2, Earth-observing nanosatellite. This paper describes the requirements of APST, trade-off for the selection of image sensor, optics, and baffle design. In addition, a survey of algorithms for star trackers and a comparison of the specifications of APST with other Pico star trackers are detailed. The field of view (FOV) estimation shows that 17°and 22°are suitable for APST and this reduces stray light problems. To achieve the 100% sky coverage, the FOV of 17°and 22°should able to detect the 5.85 and 5.35 visual magnitude of stars, respectively. It is validated by estimating the signal to noise ratio of APST and night sky test results. The maximum earth stray light angle is estimated to be 68°and a miniaturized baffle is designed with the exclusion angle of 27°.

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