Analyzing data on-board a spacecraft as it is collected enables several advanced spacecraft capabilities, such as prioritizing observations to make the best use of limited bandwidth and reacting to dynamic events as they happen. In this paper, we describe how we addressed the unique challenges associated with on-board mining of data as it is collected: uncalibrated data, noisy observations, and severe limitations on computational and memory resources. The goal of this effort, which falls into the emerging application area of spacecraft-based data mining, was to study three specific science phenomena on Mars. Following previous work that used a linear support vector machine (SVM) on-board the Earth Observing 1 (EO-1)spacecraft, we developed three data mining techniques for use on-board the Mars Odyssey spacecraft. These methods range from simple thresholding to state-of-the-art reduced-set SVM technology. We tested these algorithms on archived data in a flight software testbed. We also describe a significant, serendipitous science discovery of this data mining effort: the confirmation of a water ice annulus around the north polar cap of Mars. We conclude with a discussion on lessons learned in developing algorithms for use on-board a spacecraft.
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