CVIVAug 28, 2023

Direct initial orbit determination

arXiv:2308.14298v11 citationsh-index: 41
Originality Incremental advance
AI Analysis

This addresses the under-utilization of data in space object tracking, offering a more accurate approach for astronomers and space agencies, though it appears incremental as it builds on existing IOD methods.

The paper tackles the problem of initial orbit determination (IOD) by proposing a direct method called D-IOD that fits orbital parameters directly on observed streak images, avoiding line-of-sight extraction, and demonstrates its effectiveness on simulated and real data.

Initial orbit determination (IOD) is an important early step in the processing chain that makes sense of and reconciles the multiple optical observations of a resident space object. IOD methods generally operate on line-of-sight (LOS) vectors extracted from images of the object, hence the LOS vectors can be seen as discrete point samples of the raw optical measurements. Typically, the number of LOS vectors used by an IOD method is much smaller than the available measurements (\ie, the set of pixel intensity values), hence current IOD methods arguably under-utilize the rich information present in the data. In this paper, we propose a \emph{direct} IOD method called D-IOD that fits the orbital parameters directly on the observed streak images, without requiring LOS extraction. Since it does not utilize LOS vectors, D-IOD avoids potential inaccuracies or errors due to an imperfect LOS extraction step. Two innovations underpin our novel orbit-fitting paradigm: first, we introduce a novel non-linear least-squares objective function that computes the loss between the candidate-orbit-generated streak images and the observed streak images. Second, the objective function is minimized with a gradient descent approach that is embedded in our proposed optimization strategies designed for streak images. We demonstrate the effectiveness of D-IOD on a variety of simulated scenarios and challenging real streak images.

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