基于实时卷积神经网络的立方星星敏感器星点检测与质心计算方法
Real-Time Convolutional Neural Network-Based Star Detection and Centroiding Method for CubeSat Star Tracker
AI总结：
本文提出一种基于CNN的立方星星敏感器星点检测与质心计算方法，通过UNet变体生成分割图与距离图，利用最小二乘法求解三边测量问题，在低功耗边缘AI处理器上实现实时高精度质心计算，对高噪声和杂散光具有卓越鲁棒性。
摘要：
星敏感器是用于绝对姿态确定的最精确的天体传感器之一。这些设备检测捕获图像中的星点，并以亚像素精度准确计算它们在成像焦平面上的投影质心。传统的星点检测和质心计算算法通常依赖于阈值调整来检测星点像素，并使用像素亮度加权来计算质心。然而，高传感器噪声和杂散光等挑战会损害算法性能。本文介绍了一种基于卷积神经网络（CNN）的方法，用于星点检测和质心计算，旨在解决存在杂散光和其他伪影时噪声星敏感器图像所带来的问题。该CNN使用叠加了真实传感器噪声和杂散光的模拟星点图像进行训练，生成一个区分星点像素与背景的二元分割图，以及一个指示每个像素到最近星点质心距离的距离图。利用此距离信息和像素坐标，将质心计算转化为一组可通过最小二乘法求解的三边测量问题。我们的方法采用高效的UNet变体作为底层CNN架构，并评估了这些变体的性能。通过合成图像评估、硬件在环评估和夜空测试进行了全面测试。测试一致表明，我们的方法在质心计算精度上优于几种现有算法，并对高传感器噪声和杂散光干扰表现出卓越的鲁棒性。我们算法的另一个优点是它们可以在低功耗边缘AI处理器上实时执行。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
