A novel star identification algorithm using pattern vector

Grid algorithm is a classical star identification algorithm based on star pattern. A three-dimensional grid algorithm for all-sky autonomous star identification is proposed, which is associated with the information of star view magnitude. In contrast with traditional grid algorithm that constructs the grid cells on two-dimensional plane (e.g. x-y coordinate plane), the proposed approach makes use of the star view magnitudes of the neighboring stars as the third dimension (e.g. z-axis). A pattern is generated for each of its three-dimensional grid cells that contain neighboring star are 1, and those without are 0. The progress of star identification is to determine which pattern in the database is associated with the particular sensor pattern. Simulation shows that this method can achieve identification rate of 98.0% while the standard deviation of star position error and star view magnitudes are 1 pixel and 0.3Mv respectively. Compared with the traditional grid algorithm, the identification rate is higher, and the average runtime is 50 percent shorter.

A robust star identification algorithm with star shortlisting

The concept of software-defined satellite (SDS) enables in-orbit software reconfiguration and payload virtualization, significantly enhancing the configurability and cross-mission reusability of imaging payloads. However, this flexibility also exposes traditional star identification methods-which depend on fixed intrinsic camera parameters-to severe robustness limitations when dealing with multiple cameras and varying operational conditions. Therefore, a universal and adaptive star identification algorithm is essential to ensure stable and precise attitude determination within the SDS architecture. To this end, this paper proposes a parameter-independent star identification algorithm based on topological invariance features. The method integrates a topological invariant feature coordinate system with a self-organizing map (SOM) neural network, enabling consistent recognition across diverse payload configurations and observation scenarios. Extensive experiments under conditions such as stellar position noise, false-star noise, missing-star noise, focal length variation, and dynamic fields of view demonstrate that the proposed algorithm consistently outperforms three benchmark methods and maintains stable accuracy. These results confirm that the proposed method not only aligns with the SDS philosophy of hardware-software decoupling but also provides a necessary algorithmic foundation for its flexible, reconfigurable, and multi-payload attitude determination framework.

Star trackers are currently the most accurate spacecraft attitude sensors. As a result, they are widely used in remote sensing satellites. Since traditional charge-coupled device (CCD)-based star trackers have a limited sensitivity range and dynamic range, the matching process for a star tracker is typically not very sensitive to star brightness. For active pixel sensor (APS) star trackers, the intensity of an imaged star is valuable information that can be used in star identification process. In this paper an improved brightness referenced star identification algorithm is presented. This algorithm utilizes the k-vector search theory and adds imaged stars' intensities to narrow the search scope and therefore increase the efficiency of the matching process. Based on different imaging conditions (slew, bright bodies, etc.) the developed matching algorithm operates in one of two identification modes: a three-star mode, and a four-star mode. If the reference bright stars (the stars brighter than three magnitude) show up, the algorithm runs the three-star mode and efficiency is further improved. The proposed method was compared with other two distinctive methods the pyramid and geometric voting methods. All three methods were tested with simulation data and actual in orbit data from the APS star tracker of ZY-3. Using a catalog composed of 1500 stars, the results show that without false stars the efficiency of this new method is 4∼5 times that of the pyramid method and 35∼37 times that of the geometric method.

The reliability of star sensor in a harsh environment has recently become a research hotspot. In some harsh environment such as plume interference, a large number of false stars can be observed, leading to failure in star identification. In this paper, we propose a false star filtering algorithm, which can be used as a preprocessing algorithm for any existing star identification algorithm. By utilizing the difference between the motion of false stars and true stars, the algorithm performs angular distance tracking and star voting on multiple consecutive frames of star images and achieves false star filtering. The software simulation results show that for the star images containing more than 700 false stars, the algorithm is able to find out all true stars in less than 10 frames, and the success rate of the algorithm remains high when the star sensor rotates at up to 1°/s. The algorithm is also implemented on an existing star senor and evaluated with star images generated by a dynamic star simulator. The experimental results indicate that with the help of the proposed algorithm, the robustness of a normal star identification algorithm can be significantly improved.

A voting-based star identification algorithm utilizing local and global distribution

Ellipticity pivot star method for autonomous star identification

Typical GN&C solutions for precision pointing spacecraft require a pair of orthogonally oriented star sensors for attitude estimation and three-axis gyroscopes to provide angular rate information. The combined sensor suite however, leads to large weight, volume and power consumption. The SG100 solution seeks to eliminate the need for a separate gyroscope sensor by deriving angular velocities based on star measurements alone. The SG100 is capable of acquiring images of stars as faint as visual magnitude of 10.0 at exposure times less than 10ms and contains proprietary robust star identification, centroiding, attitude estimation and filtering algorithms to estimate precision attitude and drift-free angular rates at 100Hz. In this paper we report the development and testing of the SG100 engineering model. Results from the radiation testing (total ionizing dose) of critical components, night sky sensitivity, noise and angular rate measurement tests are presented. Test results show that the SG100 exceeds the requirements on the star sensitivity and noise equivalent angles while providing an accurate estimation of the angular rates.

This paper uses a radial basis function (RBF) neural network for star identification. A feature vector consisting of distance and angle cosine values is constructed as a library of recognition feature stars and input to the neural network. Then, the network’s output is the number of the corresponding primary star. Comparing the training efficiency and robustness of the grid algorithm with the BP neural network and radial basis function neural network for star identification, the experimental results show that the radial basis function neural network has a strong anti-interference ability against noise and pseudo-star interference. The following conclusions are drawn from the simulation tests: the sample recognition rate of radial basis function neural network algorithm can reach 96% for star position and star addition noise; when the proportion of pseudo-star reaches 20%, the sample recognition rate of radial basis function neural network algorithm is still higher than grid algorithm and BP neural network, which indicates that the radial basis function neural network algorithm has the solid anti-interference ability to both noise and pseudo-star. Therefore, the radial basis function neural network algorithm based on star Identification has specific application prospects.

The successful identification of stars is a fundamental prerequisite for satellite attitude determination by star sensors. Conventional star identification algorithms typically construct specific subgraphs using a set of bright stars or generate patterns based on angular distances between stars and their neighboring stars. However, these methods often fail when insufficient detectable stars or bright noncooperative space objects are in the field of view (FOV). Seldom studies use angular distance matching among all stars for direct identification as it is complex to calculate and store. To solve the problem, we proposed a graph algorithm based on angular distance matching score transfer, which utilizes a graph data structure, where nodes store angular distance matching scores between stars and transfer scores through graph edge weights, overcoming the limitations of incomplete utilization of angular distance matching results. We also established a math model for edge weights based on the probabilities of star pair occurrences. Simulation tests and night sky image experiments demonstrate the robustness of this algorithm against position errors, brightness errors, and fake stars. By equally using information from all sensor stars, even in cases with multiple missing stars, numerous fake stars, and extremely bright interfering stars, the identification rate remains consistently above 99.53%. This algorithm represents a highly robust star identification method and lays the foundation for future research in intelligent sensing of space environmental objects using star sensors.

To endow a star sensor with strong robustness, low algorithm complexity, and a small database, this paper proposes an all-sky star identification algorithm based on spherical polar pattern matching. The proposed algorithm consists of three main steps. First, the guide star is rotated to be a polar star, and the polar and azimuth angles of neighboring stars are used as polar pattern elements of the guide star. Then, the relative azimuth histogram is applied to the spherical polar pattern matching, and a star pair after spherical polar pattern matching is identified through angular distance cross-verification. Finally, a reference star image is generated from the identified star pair to complete the matching process of all guide stars in the field of view. The proposed algorithm is verified by simulation experiments. The simulation results show that for a star sensor with a medium field of view (15° × 15°, 1024 × 1024 pixel) and a limiting magnitude of 6.0 Mv, the required database size is 161 KB. When false and missing star spots account for 50% of the guide stars and the star spot extraction error is 1.0 pixel, the average star identification time is 0.35 ms (@i7-4790), and the identification probability is 99.9%. However, when false and missing star spots account for 100% of the guide stars and the star spot extraction error is 5.0 pixel, the average star identification time is less than 2.0 ms, and the identification probability is 97.1%.

A star pattern identification algorithm, called modified grid algorithm, for attitude determination of spacecraft is addressed. The proposed algorithm is closely connected to a general pattern recognition technique. Each star is characterized by a well-defined pattern that can be determined by the surrounding stars. The so-called grid algorithm is one of the star identification algorithms based upon the pattern recognition approach. The proposed algorithm, is motivated by the conventional grid algorithm. To enhance the performance of the conventional grid algorithm, a modified method using polar grid, virtual grid, and multi-references is proposed. Simulation study is conducted for the demonstration of the new algorithm. The proposed modified grid approach turns out to make the grid algorithm more robust and reliable.

Star identification can be accomplished by several different available algorithms that identify the stars observed by a star tracker. However, efficiency and reliability remain key issues and the availability of new active pixel cameras requires new approaches. Two novel algorithms for recursive mode star identification are presented here. The first approach is derived by the spherical polygon search (SP-search) algorithm, it was used to access all the cataloged stars observed by the sensor field-of-view (FOV) and recursively add/remove candidate cataloged stars according to the predicted image motion induced by camera attitude dynamics. Star identification is then accomplished by a star pattern matching technique which identifies the observed stars in the reference catalog. The second method uses star neighborhood information and a catalog neighborhood pointer matrix to access the star catalog. In the recursive star identification process, and under the assumption of "slow" attitude dynamics, only the stars in the neighborhood of previously identified stars are considered for star identification in the succeeding frames. Numerical tests are performed to validate the absolute and relative efficiency of the proposed methods.

The authors of this paper propose a robust star identification algorithm for a ‘Lost-In-Space’-mode star tracker for lost-cost CubeSat missions. A two-step identification framework and an embedded validation mechanism were designed to accelerate the process. In the first step, a masked distance map is designed to provide a shortlist of stars, and the embedded fast validation process enables the direct output of validated stars before the second step. In the second step, local similarity is utilized to select a set of stars from those shortlisted, and the final validation procedure rejects all unsatisfactory stars. This algorithm can provide reliable and robust recognition even when the captured star images include severe star positioning errors, missing stars and false stars. The proposed algorithm was verified by a simulation study under various conditions. As low-cost star sensors face harsh and unknown environments during deep space CubeSat missions such as asteroid exploration, the proposed algorithm with high robustness will provide an important function.

A full-sky autonomous star identification is presented. Star pattern is generated in the form of radial and cyclic features. Since radial pattern is a reliable feature, it is used for initial match, and cyclic pattern is then used in re-match step. Simulation is done to verify the performance and the result shows this method is more robust against location error compared with grid algorithm under the same condition.