Star pattern identification aboard an inertially stabilized aircraft

Abstract

Introduction P attitude determination of spacecraft is achieved through the use of star sensors. The use of star-sensor data requires the identification of stars from observed patterns by the sensor, which are then compared with known patterns obtained from a star catalog that contains the position, magnitude, spectral classification, and other data, often for stars of magnitude down to 7 or 9. A study of the efficacy of various star identification algorithms is the subject of this paper. The present studies were carried in support of the joint French-Soviet Gamma and Granat spacecraft. The Gamma spacecraft carries a rastering star sensor with an image dissector tube that operates in search mode only and records the position and magnitude of star positions and magnitudes downlinked in a rastering cycle; only four stars are available for each cycle. This Gamma star sensor is able to detect stars down to magnitude 9. The star sensor on board the Granat spacecraft is capable of detecting simultaneously the positions and magnitudes of as many as 30 stars of magnitude 6 or brighter. Most of the algorithms for star identification in the literature' were developed for the identification of measured stars by slit star sensors often used onboard spinning spacecraft, for which the attitude accuracy requirements are often quite modest. In this work, we describe candidate algorithms developed for the aforementioned projects that were tested using observations taken with Earth-bound star sensors. One of these algorithms, the polygon-match technique, is discussed briefly by Gottlieb. This technique is also used by Junkins et al. for pairs of stars, each observed from a different sensor. The other algorithms have been developed especially for this study and use angular and vectorial matching techniques. In the particular case of the Gamma spacecraft, the magnitudes of the stars were included in the algorithms for unambiguous identification in the Milky-Way regions. The first part of this paper presents the candidate algorithms on an intuitive basis. In the second part, a statistical analysis of these algorithms is carried out to examine their efficiencies.