Continuous Low-Thrust Maneuver Parameter Detection of Non-Cooperative Satellites Based on a Diffusion Model

It is challenging to detect continuous low-thrust maneuver parameters of non-cooperative satellites because the signals are weak over limited observation arcs and are readily masked by measurement noise and orbit-determination errors. This paper proposes a conditional diffusion model for detecting and estimating continuous low-thrust maneuver parameters from relative-orbit observations. The method uses relative-orbit observations of the non-cooperative target to construct conditional inputs that incorporate orbital dynamical priors. Single-step differencing and dimensionless processing are then used to strengthen weak maneuver signatures. The conditional diffusion model learns the evolution of maneuver parameters under noisy conditions and estimates three-axis continuous low-thrust acceleration sequences. Based on simulations considering the Gaussian noise of relative positions and velocities, the proposed method achieved 85.2% maneuver detection accuracy, while that of the batch least-squares benchmark method was 67.8%. The proposed method is simulated and verified based on Sentinel-6A. Results show that the continuous low-thrust maneuver can be robustly identified under low signal-to-noise ratios and the temporal parameter evolution can be recovered. The method provides a practical route for analyzing non-cooperative satellite maneuver and supporting on-orbit space situational awareness.