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。

基于实时卷积神经网络的立方卫星星辨识与中心定位方法

Real-Time Convolutional Neural Network-Based Star Detection and  Centroiding Method for CubeSat Star Tracker

Star trackers are state-of-the-art attitude estimation devices which function
by recognising and tracking star patterns. Most commercial star trackers use
conventional optical sensors. A recent alternative is to use event sensors,
which could enable more energy efficient and faster star trackers. However,
this demands new algorithms that can efficiently cope with high-speed
asynchronous data, and are feasible on resource-constrained computing
platforms. To this end, we propose an event-based processing approach for star
tracking. Our technique operates on the event stream from a star field, by
using multiresolution Hough Transforms to time-progressively integrate event
data and produce accurate relative rotations. Optimisation via rotation
averaging is then used to fuse the relative rotations and jointly refine the
absolute orientations. Our technique is designed to be feasible for
asynchronous operation on standard hardware. Moreover, compared to
state-of-the-art event-based motion estimation schemes, our technique is much
more efficient and accurate.

本文提出了一种基于事件处理的星敏感器处理方法，通过多分辨率霍夫变换和旋转平均优化技术，能够在异步环境下使用标准硬件快速、准确地跟踪恒星方向。

多分辨率渐进式 Hough 变换的基于事件的星跟踪

Event-based Star Tracking via Multiresolution Progressive Hough  Transforms

Star trackers are primarily optical devices that are used to estimate the
attitude of a spacecraft by recognising and tracking star patterns. Currently,
most star trackers use conventional optical sensors. In this application paper,
we propose the usage of event sensors for star tracking. There are potentially
two benefits of using event sensors for star tracking: lower power consumption
and higher operating speeds. Our main contribution is to formulate an
algorithmic pipeline for star tracking from event data that includes novel
formulations of rotation averaging and bundle adjustment. In addition, we also
release with this paper a dataset for star tracking using event cameras. With
this work, we introduce the problem of star tracking using event cameras to the
computer vision community, whose expertise in SLAM and geometric optimisation
can be brought to bear on this commercially important application.

本文提出使用事件传感器进行星点跟踪，提出了一种算法流程，包括旋转平均和捆绑调整。同时，还发布了一个使用事件相机进行星点跟踪的数据集，此方法引入了星点跟踪问题并将计算机视觉领域的专业知识运用于商业应用中。