一种新的星图中星获取算法

New star acquisition algorithm and optimization

提出一种新的星图中星获取算法——极值点法,利用最小二乘向量机(LSSVM)对原始星图的局部区域作灰度曲面最佳拟合,在拟合曲面上求解灰度极大值的像素点,获得星的中心点的初步位置。以初步位置为基础的星图像素聚类加速了星图中星的获取过程。以模拟星图中星的精确中心位置为参考,计算在不同噪声条件下测量位置与最近参考位置的距离平方倒数和的均值,优化 LSSVM 参数。为获得最佳星获取性能,卷积核为5×5像素的高斯LSSVM参数(σ2, γ)取(17,1.25)。该极值点法与矢量法相比,效率相当,但性能更好。

A new star acquisition algorithm i.e., extremal point approach is proposed. The method, by making use of Least Square Support Vector Machine (LSSVM) well fits the star image intensity surface over the neighborhood, and determines the maximal extremal points on the fitted surface, and then obtains possible star centers. Star cluster grouping from the candidate star centers speeds up the procedure of star acquisition. For simulated star image where the exact location of stars is known, the average sum of invert square distance between a declared star center and the nearest ideal star center, as the merit figure of star acquisition algorithms, is calculated under different noisy conditions, thus optimizing LSSVM parameters. To maximize the merit figure, when the kernel size is 5 × 5 pixels, the optimal configuration of parameters (σ2,γ) for the LSSVM with Gaussian kernel is (17, 1.25). The proposed algorithm has similar efficiency to vector method while providing higher merit figure.