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Journal of Automation and Information Sciences

Выходит 12 номеров в год

ISSN Печать: 1064-2315

ISSN Онлайн: 2163-9337

SJR: 0.173 SNIP: 0.588 CiteScore™:: 2

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Removal of Anomalous Errors of In-Flight Geometric Calibration

Том 51, Выпуск 11, 2019, pp. 14-21
DOI: 10.1615/JAutomatInfScien.v51.i11.20
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Краткое описание

An in-flight geometric calibration (further − calibration) is considered here as a procedure of refining mutual attitude parameters of onboard imaging camera and a star tracker. The calibration problem is solved using observations of geo-referenced landmarks from the orbit. Usually the above mentioned procedure is preceded by preflight calibration which is performed in lab or industrial conditions with proper equipment and is technologically relatively complicated. A necessity of in-flight geometric calibration exists, for instance, when a preflight calibration does not ensure an acceptable accuracy of ground objects geo-referencing by space snapshots received via an optical-electronic complex or if uncertainty of camera angular position relatively to a star tracker accumulates during a spacecraft operation in the orbit. Obviously in general it is necessary to take into account a possible occurrence of inadmissibly large unknown errors in accessible estimates of camera and star tracker mutual attitude parameters. Usually construction of measurement equations is accompanied by their linearization. Neglected nonlinear effects may strictly limit an achievable calibration accuracy outside a domain of estimates convergence if quite large initial errors exist. In such situations it is desirable to apply a method of revealing and exclusion of unacceptably large calibration errors. Versions of methods to be applied for such purpose are developed in this work. They are based on high characteristics of estimation algorithm convergence - fuzzy state observer and the succession of calculations in which each measurement equation instead of an invariable initial angular error takes into account and estimates its remainder after processing previous measurements. Such succession decreases the unaccounted nonlinear component of the error and thereby improves convergence of estimates. After processing of all accessible measurements and correction of searched parameters the cycles of processing of the same measurements are repeated with the use of corrected parameters. Based on comparison of results of previous and following cycles the conclusion about the level of the initial error and convergence of estimates is made.

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