document: On-board analysis of uncalibrated data for a spacecraft at mars 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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