From Data Silos to Intelligent Web for RSO Recognition
B. Liu, L. Yao, Junfeng Wu
Jul 29, 2018
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Key Takeaway Key Takeaway: OntoStar effectively solves the RSO classification problem under limited or uncertain observation conditions by representing both hard and soft data, linking related objects to RSOs, and using various mined recognition rules.
Abstract
Due to the lack of data caused by limited and uncertain observations, how to classify Resident Space Object (RSO) remains to be a difficult problem. Previous RSO classifications mainly focus on the problem when the “hard data” which are obtained by physical sensors are missing. They make use of features extracted from observation data which center on RSOs themselves only and are still very limited. This paper proposes to use an RSO Ontology named OntoStar to represent hard data and soft data. This representation not only describes RSOs themselves, but also links related objects to RSOs, establishing a more comprehensive and accurate RSO description to support more accurate and robust classifications. OntoStar not only contains mined feature deducting rules to refine the RSO feature information, but also includes a variety of mined RSO recognition rules to classify RSOs based on different sets of features. Experimental results show that RSO classification based on OntoStar can effectively solve the RSO classification problem under limited or uncertain observation conditions.