A method of near real-time detection and tracking of resident space objects (RSOs) using a convolutional neural network (CNN) and linear quadratic estimator (LQE) is proposed. Advances in machine learning architecture allow the use of low-power/cost embedded devices to perform complex classification tasks. In order to reduce the costs of tracking systems, a low-cost embedded device will be used to run a CNN detection model for RSOs in unresolved images captured by a gray-scale camera and small telescope. Detection results computed in near real-time are then passed to an LQE to compute tracking updates for the telescope mount, resulting in a fully autonomous method of optical RSO detection and tracking. Keywords: Space Domain Awareness, Neural Networks, Real-Time, Object Detection, Embedded Systems.
翻译:本文提出了一种利用卷积神经网络(CNN)和线性二次估计器(LQE)进行近实时居民空间物体(RSO)检测和跟踪的方法。机器学习架构的进步使得可以使用低功耗/成本嵌入式设备执行复杂的分类任务。为了降低跟踪系统的成本,将使用低成本的嵌入式设备运行CNN检测模型,以检测灰度相机和小型望远镜拍摄的未解决图像中的RSO。计算近实时的检测结果,然后将其传递给LQE,用于计算望远镜安装的跟踪更新,从而实现了完全自主的光学RSO检测和跟踪方法。关键词:空间领域意识,神经网络,实时,目标检测,嵌入式系统。