一种高噪音情况下的星点聚心算法

Star Centroiding Algorithm for High-noising Situation

针对严重噪音污染情况下星图的星点聚心问题,提出一种利用子窗口灰度分布判别全局背景阈值的聚心算法。根据星图特性在星图中随机选择一定数量和大小的子窗口,统计每个子窗口灰度分布并计算离散度,根据计算结果判断子窗口是否适合背景阈值计算,决定是否重新进行窗口选择,使得计算出的背景阈值能更真实的反映全局背景信息,并通过带阈值的质心平方加权算法进行星点聚心运算。仿真实验表明,算法具有较好性能和一定的抗干扰性。

Considering the difficulties in star centroiding computation of star image that is polluted by strong noises, a centroiding algorithm based on threshold judging with sub-window gray distribution static evaluation was proposed. With certain number of sub-windows with certain size chosen based on the image character, the gray distribution was studied and the decentralization was calculated. After the judgment of decentralization according certain rules, sub-windows which contain peak noise regions or those unfavorable would be displaced by new chose ones. Hence, the threshold gray value final gained would represent the comprehensive character of the star image and the succeeding application of square-weighted subdivision algorithm could improve the succeeding centroiding algorithm＇s accuracy. Simulation and result analysis proved a pleasant performance and robust capacity against sever peak noises of the proposed algorithm.