School of Instrumentation Science and Opto-electronics Engineering, Beihang University, 37 Xueyuan Rd., Haidian District, Beijing 100191, China.
Sensors (Basel). 2018 Aug 28;18(9):2836. doi: 10.3390/s18092836.
The most accurate star centroiding method for star sensors is the Gaussian fitting (GF) algorithm, because the intensity distribution of a star spot conforms to the Gaussian function, but the computational complexity of GF is too high for real-time applications. In this paper, we develop the fast Gaussian fitting method (FGF), which approximates the solution of the GF in a closed-form, thus significantly speeding up the GF algorithm. Based on the fast Gaussian fitting method, a novel star centroiding algorithm is proposed, which sequentially performs the FGF twice to calculate the star centroid: the first FGF step roughly calculates the Gaussian parameters of a star spot and the noise intensity of each pixel; subsequently the second FGF accurately calculates the star centroid utilizing the noise intensity provided in the first step. In this way, the proposed algorithm achieves both high accuracy and high efficiency. Both simulated star images and star sensor images are used to verify the performance of the algorithm. Experimental results show that the accuracy of the proposed algorithm is almost the same as the GF algorithm, higher than most existing centroiding algorithms, meanwhile, the proposed algorithm is about 15 times faster than the GF algorithm, making it suitable for real-time applications.
用于星敏感器的最精确的星点质心法是高斯拟合（GF）算法，因为星点的强度分布符合高斯函数，但 GF 的计算复杂度对于实时应用来说太高了。在本文中，我们开发了快速高斯拟合方法（FGF），它以封闭形式逼近 GF 的解，从而大大加快了 GF 算法的速度。基于快速高斯拟合方法，提出了一种新的星点质心法，它通过两次执行 FGF 来计算星点质心：第一次 FGF 步骤大致计算星点的高斯参数和每个像素的噪声强度；随后，第二次 FGF 利用第一步提供的噪声强度精确计算星点质心。这样，该算法既实现了高精度又实现了高效率。模拟星像和星敏感器图像都被用来验证算法的性能。实验结果表明，所提出算法的精度几乎与 GF 算法相同，高于大多数现有的质心法，同时，所提出的算法比 GF 算法快约 15 倍，使其适用于实时应用。
