Improved autonomous star identification algorithm

The log-polar transform （LPT） is introduced into the star identification because of its rotation invariance. An improved autonomous star identification algorithm is proposed in this paper to avoid the circular shift of the feature vector and to reduce the time consumed in the star identification algorithm using LPT. In the proposed algorithm, the star pattern of the same navigation star remains unchanged when the stellar image is rotated, which makes it able to reduce the star identification time. The logarithmic values of the plane distances between the navigation and its neighbor stars are adopted to structure the feature vector of the navigation star, which enhances the robustness of star identification. In addition, some efforts are made to make it able to find the identification result with fewer comparisons, instead of searching the whole feature database. The simulation results demonstrate that the proposed algorithm can effectively acceldrate the star identification. Moreover, the recognition rate and robustness by the proposed algorithm are better than those by the LPT algorithm and the modified grid algorithm.

An artificial intelligence enhanced star identification algorithm

An artificial intelligence enhanced star identification algorithm is proposed for star trackers in lost-inspace mode.A convolutional neural network model based on Vgg16 is used in the artificial intelligence algorithm to classify star images.The training dataset is constructed to achieve the networks’optimal performance.Simulation results show that the proposed algorithm is highly robust to many kinds of noise,including position noise,magnitude noise,false stars,and the tracker’s angular velocity.With a deep convolutional neural network,the identification accuracy is maintained at 96%despite noise and interruptions,which is a significant improvement to traditional pyramid and grid algorithms.

Near-infrared studies of nova V5584 Sgr in the pre-maximum and early decline phase

We present near-infrared spectroscopic and photometric observations of nova V5584 Sgr taken during the first 12 d following its discovery on Oct. 26.439 UT2009. The evolution of the spectra is shown from the initial P Cygni phase to an emission line phase. The prominent carbon lines seen in the JHK spectra closely match those observed in an Fe II class nova outburst. The spectra show first-overtone CO bands in emission between 2.29-2.40 μm. By examining WISE and other publicly available data, we show that the nova underwent a pronounced dust formation phase between February- April 2010.

Brightness Independent 4-Star Matching Algorithm for Lost-in-Space 3-Axis Attitude Acquisition

A star identification algorithm was developed for a charge-coupled device （CCD） or complementary metal-oxide-semiconductor （CMOS） autonomous star tracker to acquire 3-axis attitude information for a lost-in-space spacecraft. The algorithm took advantage of an efficient on-board database and an original “4- star matching” pattern recognition strategy to achieve fast and reliable star identification. The on-board database was composed of a brightness independent guide star catalog （mission catalog） and a K-vector star pair catalog. The star pattern recognition method involved direct location of star pair candidates and a sim- ple array matching procedure. Tests of the algorithm with a CMOS active pixel sensor （APS） star tracker result in a 99.9% success rate for star identification for lost-in-space 3-axis attitude acquisition when the angular measurement accuracy of the star tracker is at least 0.01°. The brightness independent algorithm requires relatively higher measurement accuracy of the star apparent positions that can be easily achieved by CCD or CMOS sensors along with subpixel centroiding techniques.

Estimating camera parameters from starry night photographs

We propose an efficient, specific method for estimating camera parameters from a single starry night image. Such an image consists of a collection of disks representing stars, so traditional estimation methods for common pictures do not work. Our method uses a database, a star catalog, that stores the positions of stars on the celestial sphere. Our method computes magnitudes(i.e., brightnesses) of stars in the input image and uses them to find the corresponding stars in the star catalog. Camera parameters can then be estimated by a simple geometric calculation. Our method is over ten times faster and more accurate than a previous method.