关键词: 空间态势感知, 无源探测, 机动检测, 归一化新息, 无监督学习

Abstract: To address the problems of large detection delays and low accuracy in detecting orbital maneuvers of incoming targets in autonomous passive detection and early warning missions for high-orbit satellites, this paper proposes an intelligent detection method for orbital maneuvers based on Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection (DCdetector). First, to improve the observability of target orbital states under passive measurement conditions, a high-order perturbed nonlinear orbital dynamics model and a line-of-sight angle measurement model are established in the inertial frame, which together form a passive detection relative navigation system. Then, a relative navigation estimation algorithm based on the square-root cubature Kalman filter is designed, and a normalized innovation reflecting the state estimation bias is constructed to characterize the target maneuver behavior. Next, an unsupervised DCdetector neural network maneuver detection model is designed using the normalized innovation sequence as input. A threshold for orbital maneuver judgment is obtained through contrastive learning training, and maneuver detection is ultimately performed by checking whether the anomaly score of the normalized innovation exceeds this threshold. Finally, numerical simulation experiments and performance analysis are conducted to validate the proposed method using a typical GEO orbit early warning mission scenario. Simulation results show that, with an angle measurement uncertainty of 0.005°, the proposed algorithm can detect small-magnitude pulse maneuvers of 0.5m·s–1 or greater for a target at a distance of 350km with a success rate exceeding 90%, and the average maneuver detection time delay is less than 360s. Comparison with three existing detection methods demonstrates that the proposed method achieves the best performance in terms of detection success rate, stability, and temporal error, significantly improving the overall passive detection performance of orbital maneuvers for high-orbit targets.

Key words: Space Situational Awareness, Passive Detection, Maneuver Detection, Normalized Innovation, Unsupervised Learning