The most accurate star centroiding method for star sensors is the Gaussian fitting (GF) algorithm, because the intensity distribution of a star spot conforms to the Gaussian function, but the computational complexity of GF is too high for real-time applications. In this paper, we develop the fast Gaussian fitting method (FGF), which approximates the solution of the GF in a closed-form, thus significantly speeding up the GF algorithm. Based on the fast Gaussian fitting method, a novel star centroiding algorithm is proposed, which sequentially performs the FGF twice to calculate the star centroid: the first FGF step roughly calculates the Gaussian parameters of a star spot and the noise intensity of each pixel; subsequently the second FGF accurately calculates the star centroid utilizing the noise intensity provided in the first step. In this way, the proposed algorithm achieves both high accuracy and high efficiency. Both simulated star images and star sensor images are used to verify the performance of the algorithm. Experimental results show that the accuracy of the proposed algorithm is almost the same as the GF algorithm, higher than most existing centroiding algorithms, meanwhile, the proposed algorithm is about 15 times faster than the GF algorithm, making it suitable for real-time applications.

For star sensors, the difficulty in realizing attitude determination lies in accurate and robust star identification algorithms. In this paper, an efficient star identification algorithm is described, which is fast, robust, and consumes small memory resources. It mainly involves two steps: the dedicated Karhunen-Loève (K-L) transformation step and the star walk formation step. Once several pattern triangles for pole stars are built, the K-L transformation is performed to calculate the K-L values to match several rough correspondences from a predefined database. Star walks are then formed among the candidate stars using the neighboring star table, which indicates each star's geometric distribution in the neighborhood, until a unique match is found. Our algorithm can also complete an efficient verification procedure based on a fast triangle algorithm, since stars' surrounding information can be acquired from the neighboring star table. The simulation results compare favorably with two published algorithms, and the approach has been implemented on actual star images.

A fast and robust star identification algorithm based on hash map is proposed in this paper. The algorithm is derived from the classical triangle feature matching algorithm but significantly improves the efficiency and robustness. By mapping each triangle feature to an integer and building a hash map of all the triangle features, the proposed algorithm is capable of matching a triangle feature with the computational complexity only O(8); based on this characteristics, a double-triangle verification method is proposed to improve the robustness of identification; moreover, an optimized strategy for triangle feature selection is proposed, which further improves efficiency of the algorithm. Experimental results on both simulation images and night sky images show that the proposed algorithm is quite robust to false stars, position noise and magnitude noise. The identification rate of the algorithm is higher than the grid algorithm and similar to the pyramid algorithm, while the identification speed is up to 100 times faster than the pyramid algorithm.

In this article, we consider the problem of extracting star spots for traditional star sensors under highly dynamic conditions. Under this setting, it is difficult to effectively extract star spots with a low signal-to-noise ratio (SNR), resulting in a failure to estimate the attitude of star sensors. We propose a method named optimal directional connected component (ODCC) for this task. According to the dynamic characteristics of star sensors, we propose an image enhancement method that can adaptively estimate the directions of star spots and integrate the star image so that the SNR of the star spots is increased. This aids in searching for spots with a low SNR. According to the imaging characteristics of a star, we model the imaging region of a star as a rectangle and then creatively cast the problem of extracting spots into a problem of estimating the endpoints of the rectangle. A maximum likelihood estimation method based on the Gaussian mixture model is proposed to determine the region of the star spot. Experiments demonstrate the extraction capability and accuracy of the method. In conclusion, this method has a promising application value for improving the dynamic performance of traditional star sensors.

Under highly dynamic conditions, the star-spots on the image sensor of a star tracker move across many pixels during the exposure time, which will reduce star detection sensitivity and increase star location errors. However, this kind of effect can be compensated well by setting an appropriate exposure time. This paper focuses on how exposure time affects the star tracker under highly dynamic conditions and how to determine the most appropriate exposure time for this case. Firstly, the effect of exposure time on star detection sensitivity is analyzed by establishing the dynamic star-spot imaging model. Then the star location error is deduced based on the error analysis of the sub-pixel centroiding algorithm. Combining these analyses, the effect of exposure time on attitude accuracy is finally determined. Some simulations are carried out to validate these effects, and the results show that there are different optimal exposure times for different angular velocities of a star tracker with a given configuration. In addition, the results of night sky experiments using a real star tracker agree with the simulation results. The summarized regularities in this paper should prove helpful in the system design and dynamic performance evaluation of the highly dynamic star trackers.

A fast and robust star identification algorithm is proposed in this paper. The algorithm is derived from the grid algorithm but significantly improves the robustness by increasing the probability of the correct close neighbor star and reducing the pattern noise. Firstly, star pair patterns are constructed rather than star patterns. And a traversal method is adopted to find all the possible closest neighbor stars, which improves the robustness towards brightness noise. Secondly, the two-dimensional angular distance features are used instead of grid features in Cartesian coordinates to improve the robustness towards positional noise. The simulation results show that the proposed algorithm is quite robust to a variety of noise conditions. The identification rate of the proposed algorithm is higher than the grid algorithm and similar to the pyramid algorithm, while the identification speed is up to dozens times faster than the pyramid algorithm.