基于各向异性高斯曲面拟合的星点质心提取算法

Center Extraction Method for Star-Map Targets Based on Anisotropic Gaussian Surface Fitting

恒星识别以及卫星目标检测识别是空间监视系统的重要应用之一。由于星图图像点目标成像的特点以及大量背景恒星的干扰,星图中用于目标识别的特征难以提取,因此目标的位置是实现目标识别的关键特征。高斯曲面拟合方法是使用较为广泛的目标质心提取算法之一,通过理论分析和实验表明传统高斯曲面拟合方法对运动卫星的定位存在较大误差。为此,提出了各向异性的高斯曲面拟合模型,该模型通过使用两个不同的高斯模糊参数和旋转因子,可以捕捉目标不同方向的各异特征,适合卫星由于运动造成的随机方向模糊。仿真实验和真实数据实验表明,本文方法的总体定位精度可分别达到0.008和0.04,并能够准确提取星图目标的质心,相比传统方法有较大改善。

The star and satellite target detection and recognition are one of the important applications of space surveillance system. Because of the characteristic of point target imaging of the map image, and a large number of the interference of background stars, the feature extraction of the map for target recognition is difficult, so the location of the object is the key characteristics to realize target identification. Gaussian curved surface fitting method is one of the target centroid extraction algorithms to be used widely. Theoretical analysis and experiment show that the traditional Gaussian curved surface fitting method of the satellite motion poisoning has much error. So, the anisotropic Gaussian surface fitting model is put forward, and the model by using two different Gaussian blur parameters and rotation factors to capture target of anisotropic characteristics of different directions, which is suitable for the fuzzy random direction caused by the satellite movement. Simulation experiments and real data test show that the overall positioning accuracy of this method can achieve 0. 008 and 0.04, respectively, which is able to accurately extract the map target centroid, and improved greatly than that of the traditional methods.