Lost-in-space pyramid algorithm for robust star pattern recognition
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A robust method is introduced for star pattern identification. The method is demonstrated to be highly efficient and especially, a provably reliable means for solving the general lost in space problem where no prior estimate of pointing is available. At the heart of the method is the k-vector approach for accessing the star catalog, which provides a searchless means to obtain all possible cataloged stars from the whole sky that could possibly correspond to a particular measured pair, given the measured interstar angle and the measurement precision. A tiered logical structure, making use of k-vector accessed candidate stars for each measured pair, is introduced where interstar angles for triples and quadruples, and so on star patterns are matched, with an analytical expression derived for the expected frequency of randomly matching these patterns. This expected frequency can be used to rigorously terminate the star pattern matching process with an essentially certain star identification. For the first time, this paper introduces this probability-based method to characterize the likelihood of an incorrect star identification, and since the expected frequencies derived are general functions of the measurement precision, number of stars the camera can image, and the measured interstar angles, this will hereafter negate the need for expensive Monte Carlo type simulations for each star sensor design variation. In addition, we introduce a novel way to include in the star identification process binary stars that are too close together to be centroided as distinct stars. All of these developments are supported by simulations and by a few ground test experimental results.
author list (cited authors)
Mortari, D., Junkins, J. L., & Samaan, M. A.