An adaptive weighted strong tracking unscented Kalman filter is proposed in this paper for long-range relative navigation alongside non-cooperative maneuvering targets. First, an equation for obtaining the relative motion of two bodies is derived, it can be well adapted for a problem that has medium or long-distance. Secondly, a variance statistics function is introduced in the method to calculate residual weight in real time. The residual weight can be used to adjust the contribution of different measurement information to the fading factor. In this way, the sensitivity of the system to small pulse maneuvers is improved. Finally, the mean and covariance of the posterior state are calculated by the unscented transformation. A replacement equation for the fading factor is derived to improve the first-order approximation accuracy for a strong tracking system. Impulsive maneuvers with three different magnitudes are employed in a series of tests. Results from different methods showed that the proposed method could effectively detect pulse maneuvers with low latency. The proposed method is also numerically stable.

Collision probability is employed for evaluating whether there will be a dangerous encounter between 2 space objects. The fidelity of the collision probability mainly depends on the accuracies of orbit prediction and covariance prediction for the space objects. In this paper, the collision probability between the Tsinghua Gravitation and Atmosphere Science Satellite, Q-Sat, and the space debris with a North American Aerospace Defense Command ID of 49863 on 2022 January 18 was calculated. The 2 objects approached each other dangerously close and the event was reported. First, the atmospheric density model is modified by a dynamic approach-based inversion to improve the accuracy of orbit prediction for the Q-Sat. Next, predictions of position error covariance are carried out. Orbits of the next 24 hours are predicted, and the predicted orbits are compared with the actual orbits of the Q-Sat. Backpropagation neural network was trained for predicting the position error covariance. For the space debris, the 2-line element data are employed. Orbit predictions for the space debris are also conducted and compared with the actual orbit. Another backpropagation neural network for predicting the position error covariance for the space debris is trained. Using the covariances from the backpropagation neural network, the error ellipsoids of the 2 objects are established. The error ellipsoids are later projected to the encounter plane to calculate the collision probability. Different from the reports from other institutes, the closest distance between the Q-Sat and the space debris calculated by the current method was 2.71 km. The collision probability was 1.16 × 10 −11 . It was not a dangerous encounter event. The onboard precise orbit determination device enabled improved orbit determination precision and orbit prediction accuracy, which is important for space safety management.

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