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 (CNN), Kernel Principal Component Analysis (KPCA), and Model-Agnostic Meta-Learning (MAML), have been assessed with various data types. Earlier studies highlighted the combination of the YOLO object detector and a linear Kalman filter for object detection and tracking. Building on this, our project introduces CosmosDSR, a novel methodology combining YOLOv3 with an Unscented Kalman Filter for tracking satellites in sequential images, compared to a linear Kalman filter. Using the SPARK dataset from the University of Luxembourg for training and testing, the YOLOv3 precisely detected and classified all satellite categories (mAP=97.18%, F1=0.95) with few errors (TP=4163, FP=209, FN=237). Both CosmosDSR and the LKF tracked satellites accurately (UKF: MSE=2.83/RMSE=1.66, LKF: MSE=2.84/RMSE=1.66). Despite concerns of class imbalance and the absence of real images, the model shows promise. Future work should address these limitations, increase tracking sample size, and improve metrics. This research suggests the algorithm's potential in detecting and tracking satellites, paving the way for solutions to the Kessler syndrome.

翻译：凯斯勒综合征指的是频繁太空活动导致的空间碎片持续增多，对未来的太空探索构成威胁。解决这一问题至关重要。多种人工智能模型，包括卷积神经网络（CNN）、核主成分分析（KPCA）以及模型无关元学习（MAML），已针对不同类型的数据进行了评估。早期研究强调了将YOLO目标检测器与线性卡尔曼滤波结合用于目标检测与追踪的可行性。在此基础上，我们的项目提出了CosmosDSR，一种结合YOLOv3与无迹卡尔曼滤波（UKF）的新方法，用于在序列图像中追踪卫星，并与线性卡尔曼滤波（LKF）进行对比。利用卢森堡大学的SPARK数据集进行训练和测试，YOLOv3精确检测并分类了所有卫星类别（mAP=97.18%，F1=0.95），且错误率较低（TP=4163, FP=209, FN=237）。CosmosDSR与LKF均能准确追踪卫星（UKF: MSE=2.83/RMSE=1.66, LKF: MSE=2.84/RMSE=1.66）。尽管存在类别不平衡和缺乏真实图像的问题，该模型仍展现出潜力。未来工作应解决这些局限，增加追踪样本量，并改进评估指标。本研究表明该算法在卫星检测与追踪方面的潜力，为应对凯斯勒综合征的解决方案奠定了基础。