## 5 Summary  
To address the uncertainty of maneuver intention in tracking HGVs, a quantitative maneuver intention model was established first. Then, a PSLSTM neural network architecture was designed to recognize and predict the maneuver intention based on the stacked LSTM neural network. Finally, the SS-LCBBA was proposed. The loss coefficients were added to the value function of the LCBBA distributed mission planning algorithm, and a switching window selecting algorithm was designed to improve the adaptability of the LCBBA to the uncertainty of the HGV maneuver intention. The PSLSTM network can still preserve the long-term memory of the data while considering the short-term correlation. The PSLSTM network has better performance for the problem of predicting and recognizing the maneuver intentions of HGVs, as shown in the simulation results. Obtaining the maneuver intentions of HGVs can assist satellites in calculating tracking values and selecting switching times. The value loss factor can prompt the satellite to consider the uncertainty of HGVs' maneuver intentions when selecting targets to track. The sensor-switching strategy can give the satellite a reasonable time period to take over the mission. The SS-LCBBA algorithm has a higher cumulative tracking score than the baseline algorithms for HGVs. Local consensus algorithms have higher response efficiency for new tasks, but the number of satellite switches increases. Therefore, the sensor-switching strategy is an effective method to improve the tracking coherence during satellite switching. Additionally, considering the uncertainty of the HGV maneuver intention, adding loss coefficients to the value function can effectively enhance the adaptability of the planning algorithm and make the algorithm score higher. However, the method proposed in this paper also possesses the following limitations: (a) Maneuver intention prediction is limited to the short term and does not allow for long-term prediction. (b) It is assumed that the position data of the HGVs are accurate. However, in practical application scenarios, observation errors usually occur due to the uncertainty of the sensors. (c) The mission planning algorithm was simulated and tested on a civilian computer without considering the onboard hardware-in-the-loop latency. For future research topics, there are several extension directions worth exploring. (a) Design a method for dynamic selection of prediction results considering the frequency of HGVs' maneuvers. If HGVs have multiple different maneuver intentions within a prediction sequence length, the accuracy of the prediction will be degraded. Therefore, a method needs to be designed to select the effective sequence lengths in the prediction results and dynamically adjust the frequencies of maneuver intention prediction. (b) Design a new multi-satellite cooperative trajectory prediction method. In practical application scenarios, the measurement angle of sensors usually has errors. The state sequences with errors will mislead the deep neural network to output biased results. Therefore, a new multi-satellite collaborative prediction algorithm is needed to improve the accuracy of trajectory prediction through reasonable sensor scheduling and data pre-processing. (c) Design a trajectory correlation algorithm for HGVs, which have high maneuverability and multiple HGVs can change their relative positions frequently during the flight. Therefore, it is necessary to design a trajectory correlation method that can be applied to HGVs to avoid the interference of the tracking algorithm and other algorithms such as the prediction algorithm by observation data of different targets. Conceptualization, C.W. and Y.C. ; methodology, Y.C. ; software, Y.W. ; validation, Y.C. ; investigation, B.Y. ; resources, Y.W. ; data curation, Y.C. ; writing--original draft preparation, Y.C. ; writing--review and editing, Y.Z. ; visualization, Y.C. All authors have read and agreed to the published version of the manuscript.