CubeSat systems are preferred for short-term space missions, like those with a scientific objective, since they make it possible to complete them in an affordable cost. On the other hand, the resource-constrained natu...
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ISBN:
(纸本)9781450397407
CubeSat systems are preferred for short-term space missions, like those with a scientific objective, since they make it possible to complete them in an affordable cost. On the other hand, the resource-constrained nature of such systems poses significant challenges in software design, for the system payload. We focus on image compression and processing for scientific space missions, a vital functionality, due to the limited onboard storage capacity and the infrequent time periods, in which a satellite in orbit can send images to the ground, through a limited bandwidth connection. For CubeSat systems, the performance of imageprocessing depends - even more than other space systems - on trade-offs, which are influenced by size, power and complexity constraints. In this context, we present the design challenges and the image compression/processing solution developed, for a university CubeSat built to carry on a biological experiment. All changes of the performance capability in the different parts of the payload image data chain (image resolution, onboard storage capacity, communication channel bandwidth and error characteristics) are taken into account. Moreover, any decision that increases the risk of incorrect reception of images from the experiment is weighted against the benefits of improved performance. We provide experimental results that show how our imageprocessing solution resolves the associated trade-offs, while fulfilling the mission requirements.
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