The Kessler syndrome refers to the escalating space debris from frequent space activities, threatening future space exploration. Addressing this issue is vital. Several AI models, including Convolutional Neural Networks, Kernel Principal Component Analysis, and Model-Agnostic Meta- Learning have been assessed with various data types. Earlier studies highlighted the combination of the YOLO object detector and a linear Kalman filter (LKF) for object detection and tracking. Advancing this, the current paper introduces a novel methodology for the Comprehensive Orbital Surveillance and Monitoring Of Space by Detecting Satellite Residuals (CosmosDSR) by combining YOLOv3 with an Unscented Kalman Filter (UKF) for tracking satellites in sequential images. Using the Spacecraft Recognition Leveraging Knowledge of Space Environment (SPARK) dataset for training and testing, the YOLOv3 precisely detected and classified all satellite categories (Mean Average Precision=97.18%, F1=0.95) with few errors (TP=4163, FP=209, FN=237). Both CosmosDSR and an implemented LKF used for comparison tracked satellites accurately for a mean squared error (MSE) and root mean squared error (RME) of MSE=2.83/RMSE=1.66 for UKF and MSE=2.84/RMSE=1.66 for LKF. The current study is limited to images generated in a space simulation environment, but the CosmosDSR methodology shows great potential in detecting and tracking satellites, paving the way for solutions to the Kessler syndrome.

翻译：凯斯勒综合征指频繁太空活动引发的空间碎片持续增长，对未来太空探索构成威胁。解决此问题至关重要。基于卷积神经网络、核主成分分析及模型无关元学习的多种AI模型已被评估应用于不同类型数据。早期研究强调了YOLO目标检测器与线性卡尔曼滤波（LKF）在目标检测与跟踪中的结合。在此基础上，本文提出一种名为"通过卫星残骸检测实现综合轨道监控"（CosmosDSR）的新型方法论，通过将YOLOv3与无迹卡尔曼滤波（UKF）相结合，实现序列图像中的卫星跟踪。采用"利用空间环境知识识别航天器"（SPARK）数据集进行训练与测试，YOLOv3精确检测并分类了所有卫星类别（平均精度均值=97.18%，F1分数=0.95），错误率极低（真阳性=4163，假阳性=209，假阴性=237）。对比实验显示，CosmosDSR与已实现的LKF均能准确跟踪卫星，其中UKF的均方误差（MSE）与均方根误差（RMSE）分别为2.83/1.66，LKF分别为2.84/1.66。尽管本研究仅限于空间模拟环境生成的图像，但CosmosDSR方法在卫星检测与跟踪方面展现出巨大潜力，为破解凯斯勒综合征开辟了道路。