Birla Institute of Technology and Science, Pilani, K. K. Birla Goa Campus, Sancoale 403726, Goa, India.
Sensors (Basel). 2023 Mar 17;23(6):3222. doi: 10.3390/s23063222.
The localization of the center of the star image formed on a sensor array directly affects attitude estimation accuracy. This paper proposes an intuitive self-evolving centroiding algorithm, termed the sieve search algorithm (SSA), which employs the structural properties of the point spread function. This method maps the gray-scale distribution of the star image spot into a matrix. This matrix is further segmented into contiguous sub-matrices, referred to as sieves. Sieves comprise a finite number of pixels. These sieves are evaluated and ranked based on their degree of symmetry and magnitude. Every pixel in the image spot carries the accumulated score of the sieves associated with it, and the centroid is its weighted average. The performance evaluation of this algorithm is carried out using star images of varied brightness, spread radius, noise level, and centroid location. In addition, test cases are designed around particular scenarios, like non-uniform point spread function, stuck-pixel noise, and optical double stars. The proposed algorithm is compared with various long-standing and state-of-the-art centroiding algorithms. The numerical simulation results validated the effectiveness of SSA, which is suitable for small satellites with limited computational resources. The proposed algorithm is found to have precision comparable with that of fitting algorithms. As for computational overhead, the algorithm requires only basic math and simple matrix operations, resulting in a visible decrease in execution time. These attributes make SSA a fair compromise between prevailing gray-scale and fitting algorithms concerning precision, robustness, and processing time.
传感器阵列上形成的星像中心的定位直接影响姿态估计的精度。本文提出了一种直观的自进化质心法，称为筛检搜索算法（SSA），它利用点扩散函数的结构特性。该方法将星像点的灰度分布映射到一个矩阵中。这个矩阵进一步被分割成连续的子矩阵，称为筛子。筛子由有限数量的像素组成。这些筛子根据其对称性和幅度进行评估和排序。图像点中的每个像素都携带与其相关的筛子的累积得分，质心是其加权平均值。该算法的性能评估是通过使用具有不同亮度、扩展半径、噪声水平和质心位置的星像来进行的。此外，还设计了针对特定场景的测试案例，如非均匀点扩散函数、固定像素噪声和光学双星。将提出的算法与各种长期存在和最先进的质心法进行了比较。数值模拟结果验证了 SSA 的有效性，它适用于计算资源有限的小型卫星。研究发现，该算法的精度可与拟合算法相媲美。就计算开销而言，该算法只需要基本的数学和简单的矩阵运算，从而显著减少了执行时间。这些属性使得 SSA 在精度、鲁棒性和处理时间方面在现有的灰度和拟合算法之间达到了公平的折衷。
