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The Kessler syndrome refers to the escalating space debris from frequentspace activities, threatening future space exploration. Addressing this issueis vital. Several AI models, including Convolutional Neural Networks, KernelPrincipal Component Analysis, and Model-Agnostic Meta- Learning have beenassessed with various data types. Earlier studies highlighted the combinationof the YOLO object detector and a linear Kalman filter (LKF) for objectdetection and tracking. Advancing this, the current paper introduces a novelmethodology for the Comprehensive Orbital Surveillance and Monitoring Of Spaceby Detecting Satellite Residuals (CosmosDSR) by combining YOLOv3 with anUnscented 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 andclassified all satellite categories (Mean Average Precision=97.18%, F1=0.95)with few errors (TP=4163, FP=209, FN=237). Both CosmosDSR and an implementedLKF 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 andMSE=2.84/RMSE=1.66 for LKF. The current study is limited to images generated ina space simulation environment, but the CosmosDSR methodology shows greatpotential in detecting and tracking satellites, paving the way for solutions tothe Kessler syndrome.

CosmosDSR -- a methodology for automated detection and tracking of orbital debris using the Unscented Kalman Filter