With advancements in aerospace technology, remote sensing satellite imagery applications are evolving toward high precision, refinement, and commercialization. The geometric positioning accuracy of satellites has reached the meter level, imposing new requirements on satellite development and attitude determination. In remote sensing applications, acquiring accurate geometric positioning information is critical. Satellite positioning accuracy is closely tied to attitude determination precision, where a 1″ error in attitude determination can cause a 3?5 m deviation in positioning. Consequently, attitude determination accuracy has become the most critical factor limiting improvements in geometric positioning. The star camera is the most commonly used sensor in satellite attitude determination, and there are two primary methods based on star camera measurements. The first constructs an observation model using star vectors captured by the star camera and determines orientation relative to the inertial coordinate system by comparing observed and reference vectors. The second builds upon this method to obtain absolute attitude by fusing data from multiple sensors, such as gyroscopes, using filtering algorithms for high-precision measurements. Although these filtering algorithms enhance accuracy, they require additional sensors, increasing power consumption and costs, which is unsuitable for micro-satellite platforms. Furthermore, since these algorithms rely on star camera measurements for correction, their precision is directly influenced by the observation precision of the star camera. Therefore, it is of great engineering value to enhance the accuracy of single-star camera attitude determination by modeling its observations and fully utilize inter-star information to reduce errors.

To address the technical challenges outlined above, we first analyze the principles of traditional star camera attitude determination methods. This analysis reveals that accuracy primarily depends on four factors: the precision of navigation star vectors, the accuracy of matching observed stars with navigation stars, the accuracy of observed star vectors, and the weighting of star points. Navigation star vector accuracy can be improved by correcting star positions using stellar motion models, while matching accuracy can be improved via optimized star map recognition algorithms. In this paper, we focus on improving attitude accuracy by refining the precision of observed vector and optimizing the distribution of star point weights. First, considering observational noise, certain stars within the field of view (FOV) may exhibit lower measurement accuracy and negatively influence attitude results. To mitigate this, a threshold is introduced that dynamically adjusts based on real-time noise analysis of in-orbit star images, ensuring a balance between data retention and error elimination under varying conditions. Second, since the weight of a star point in the observation model correlates with its accuracy, we propose a method that evaluates observation vectors based on angular distance errors invariant across coordinate systems. Cross-validation of all FOV data enables optimal weight allocation. Finally, we build a topological model linking multiple stars, use redundant observations to construct constraint equations, and iteratively correct measurement errors using adjustment algorithms. The refined vectors and weights are then input into the quaternion estimator (QUEST) algorithm to determine the current frame’s attitude.

To evaluate the performance of the proposed method, we assess its sensitivity to errors through comparative simulations with traditional algorithms and verify its robustness under observation noise. In addition, we confirm the method’s effectiveness in practical applications using in-orbit star images captured by star camera A and star camera B on board the Wuhan-1 satellite. Simulation parameters are set based on the actual optical system design specifications of the star cameras (Table 1). The precision of star observation vectors is affected by multiple coupled error sources, which collectively act as deviations on star centroid extraction positions. These disturbances are simulated by adding positional noise of different magnitudes to theoretical star imaging positions. To reduce the influence of star distribution and density on attitude determination accuracy, all-sky imaging scenarios are simulated by randomly selecting the star camera’s pointing directions. Simulation results (Fig. 2) demonstrate that, at varying error levels, the proposed method achieves higher attitude determination accuracy and superior noise resistance compared to traditional algorithms, maintaining high precision even under poor imaging conditions. For practical validation, we process the in-orbit star images captured by the Wuhan-1 satellite’s star cameras using the proposed method. Prior to attitude determination, it is necessary to calibrate the optical parameters of the star cameras in orbit according to the actual star images. Star screening thresholds are established using star maps under both normal (Fig. 5) and high-noise imaging conditions (Fig. 6), with Starpoint retention criteria determined by measurement errors. Since we have no way of knowing the true attitude pointing of the real star images, we assess the algorithms’ attitude determination accuracy based on the following two dimensions: inter-frame attitude stability of a single star camera and optical axis angle stability between two star cameras. For inter-frame attitude stability evaluation, we analyze 3600 consecutive frames of in-orbit star images by different attitude determination algorithms. The accuracy on the X and Y axes for a single star camera, as determined by the proposed algorithm, is better than 0.55″, a 15% improvement over the traditional algorithms. For optical axis angle stability between two star cameras, data from star camera A and star camera B during four 30-s in-orbit missions are analyzed, demonstrating that the proposed method achieves precision better than 0.5″, representing a 50% improvement over traditional methods.

In this paper, we present a high-precision star camera-based attitude determination algorithm suitable for micro-satellite platforms. The proposed algorithm leverages redundant observed stars in the attitude determination process, integrates the star camera imaging model, and assesses the credibility of star observation results through the invariant characteristics of interstellar angular distances across different reference frames. The star centroid positions are corrected to achieve precise attitude determination using the star camera. Simulation experiments and real-star image measurements validate the robustness and effectiveness of the proposed method, achieving sub-arcsecond accuracy in in-orbit star image attitude determination. This paper introduces a novel technical approach for high-precision post-mission attitude determination.