Construction of Star Catalogue Based on Svm

The method of constructing navigation star catalogue always based on Magnitude Filtering Method (MFM). But it did not work well because of two typical disadvantages. On one side it would extract so many stars that there was redundancy in the catalogue. And on the other side it would generate “hole” in some area of celestial sphere. In this article, Support Vector Machine (SVM) was introduced into extracting navigation stars from basic catalogue. After using the new method on SAO catalogue, it was proved that taking SVM as the method of extracting navigation-stars has good prospection. 1. PREFACE Because of its lightness, economy and high precision in confirmation of satellite’s attitude, star sensor is becoming mainly instruments for measuring attitude on space probes. The essential of measuring satellite’s attitude based on star sensor is confirming the instantaneous orientation of star sensor axes in celestial sphere coordinate system based on starlight vectors, and then ascertaining the attitude of satellite. In above processes the star recognition is the key process. So far, except the recognition method based on neural-network, all kinds of methods promoting by scholars need to consider the construction of star catalogues. At present the popular way of constructing star catalogues is filtering based on star’s light, which only reserving the stars lighter than the giving threshold. In some sense, it is only a kind of simple linearity classification method, which cannot ensure that there are plenty of stars in every watch-field because the classification only relay on a giving threshold. In some fields there must be so many stars which makes the recognition redundancy and in some fields there is even none stars which appear a hole. So how to select navigation stars and construct a self-contained and even star catalogues based on the distributing of stars become the basis of star recognition. 2. CONSTRUCTION PRINCIPLE OF STAR CATALOGUES 2.1 Research Objective In star catalogue, except for the coordinates in celestial sphere, it includes the anniversary proper motion, anniversary and long-term movement of all the stars. At present, the star catalogues which used widely in space applications are as bellows: SAO, FK4 and Goffin catalogues etc. In the SAO catalogue, it records more than 260,000 stars’ information, including position, brightness and spectrum. Specially, the position precision of stars can achieve 10, but brightness precision is only 0.1. Because the position precision is more important than brightness precision in determining attitude of satellite, we select the SAO catalogue as basic catalogue. In order to construct a self-contained and even star catalogues, it is necessary to find out the distributing rules of all the stars firstly. With the increasing of the threshold, the number of stars appeared in every FOV (Field Of View) increase obviously. Given the threshold of star sensor as 7.0, only when every FOV include more than 4 stars, can the catalogue be self-contained, and there will be more than 16,000 stars in the catalogue. It is difficult for storing the catalogue and matching star pairs effectively. Generically, star sensor can detect stars lighter than 6.0-6.5, so we can design appropriate FOV to make self-contained star catalogue. To assure that there are more than 4 stars in every FOV, the FOV of star sensor should be larger than 14°×14°. According to normally star sensor, which the FOV is 8°×8°, there will be 11% FOVs that cannot complete star recognition, because the numbers of stars in them are lack of 4 . 2.2 Distributing Rule of Stars From the discussion above, if we want to determine attitude of satellites based on star sensors, there may be only two choices: using the star sensors which have large FOV or increasing the number of stars that storing in the satellites. But both of the two methods are low efficient. So we have to resolve the problem by different ways. Given the FOV of star sensor is 8°×8°, if in every FOV it must have stars more than some given number (N), so we can determine that in every FOV what their thresholds are, and what the distributing rule of thresholds in the whole celestial sphere is. When N=4 the distributing rule can be shown by figure.1. From the above figure, the distributing rule of thresholds actually is a spacial irregular plane. If we consider the distributing rule of thresholds as optimized plane that it can devise all stars into two types: navigation stars or non-navigation stars, so the problem of selection of navigation stars can switch to the problem of seeking non-linearity optimized classified plane. It is very difficult for some linearity classified methods, but it is advantage of SVM.