Star trackers are one of the most accurate celestial sensors used for
absolute attitude determination. The devices detect stars in captured images
and accurately compute their projected centroids on an imaging focal plane with
subpixel precision. Traditional algorithms for star detection and centroiding
often rely on threshold adjustments for star pixel detection and pixel
brightness weighting for centroid computation. However, challenges like high
sensor noise and stray light can compromise algorithm performance. This article
introduces a Convolutional Neural Network (CNN)-based approach for star
detection and centroiding, tailored to address the issues posed by noisy star
tracker images in the presence of stray light and other artifacts. Trained
using simulated star images overlayed with real sensor noise and stray light,
the CNN produces both a binary segmentation map distinguishing star pixels from
the background and a distance map indicating each pixel's proximity to the
nearest star centroid. Leveraging this distance information alongside pixel
coordinates transforms centroid calculations into a set of trilateration
problems solvable via the least squares method. Our method employs efficient
UNet variants for the underlying CNN architectures, and the variants'
performances are evaluated. Comprehensive testing has been undertaken with
synthetic image evaluations, hardware-in-the-loop assessments, and night sky
tests. The tests consistently demonstrated that our method outperforms several
existing algorithms in centroiding accuracy and exhibits superior resilience to
high sensor noise and stray light interference. An additional benefit of our
algorithms is that they can be executed in real-time on low-power edge AI
processors.

使用卷积神经网络（CNN）的星空追踪器算法，通过模拟星像覆盖真实噪声和杂光，并结合像素坐标信息进行质心计算，解决高噪声和杂光干扰下的星点检测与定位问题，具有较高准确性和优秀的抗干扰能力。此外，该算法可在低功耗边缘 AI 处理器上实时执行。