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

Abstract

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.