The pointing accuracy of a telescope is a fundamental performance metric, and the prevalent method for correcting telescope pointing deviations involves selecting stars that are uniformly distributed across the sky, measuring the difference between the telescope's observed calibration position and the expected position from a star catalog, and then applying correction models to compensate for the deviations. However, the uniformity of traditional star selection algorithms can be suboptimal. To address this issue, this study proposes a star uniform selection algorithm based on maximizing Kozachenko-Leonenko entropy (MKLE). Specifically, the proposed algorithm employs KL entropy computed based on the k-nearest neighbor approach as a metric of uniformity, where a higher KL entropy indicates more uniform star selections. To adapt the algorithm to the cylindrical surface formed by the telescope's azimuth and elevation axes, a KD-tree search strategy is proposed. Furthermore, to enhance the uniformity of selected stars, a simulated annealing method is utilized to maximize the KL entropy and select the most uniformly distributed stars. The performance of this method was verified by calculating the KL entropy using MKLE, rectangular grid method (RGM), and first-order self-organizing selection method (SO). The results indicate that the MKLE algorithm outperforms the other methods in terms of uniformity of star selection.
Traditional focal length measurement is usually limited to small-aperture optical systems, and relies on human visual judgment to have subjective errors. In contrast, the digital optical parameter testing software system is based on modern equipment, such as a large-area CCD camera, a motor-driven precision translation stage, a large-diameter high-quality collimator, and a high-precision three-dimensional turntable. High-precision measuring system. The test system uses the magnification method, based on the sharpness evaluation function, the variable step length climbing focusing search algorithm, and the Mexican hat multi-directional wavelet transform function image edge detection processing algorithm to achieve focal length measurement, improve the accuracy and efficiency of parameter measurement, and also Complete the framework construction for the follow-up optical parameter comprehensive test system.
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