TY - GEN
T1 - Prediction Analysis of Target State in Spacecraft Pursuit Game
AU - Zhen, Zhang
AU - Chong, Sun
AU - Jianlin, Chen
AU - Xiaolong, Wang
AU - Qun, Fang
AU - Zhanxia, Zhu
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In the process of the spacecraft's pursuit and escape game, it is important to predict the motion state of the target quickly and accurately. However, in the process of spacecraft pursuit game, the dynamics of spacecraft targets show complex nonlinear characteristics under a variety of perturbation. It is very difficult to predict the motion state information because of the noise or the uncertain parameters in the observation. In general, Monte Carlo method is used to estimate the motion state of the target when there is uncertainly. Monte Carlo method analyzes the target state information by traversing uncertain parameters and large-scale repetitive numerical integration, which means the Monte Carlo method is very expensive. To solve this problem, this paper proposes a semi-analytical spacecraft target state estimation method based on polynomial approximation. This method divides the spacecraft dynamic parameters with parameter uncertainty into a small number of nominal point parameters and a large number of other associated point parameters. Orbit prediction for nominal point parameters is solved by numerical integration to ensure prediction accuracy. For other related point state prediction problems, the polynomial Taylor expansion semi-analytical method is used to solve the problem, which can greatly reduce the repeated numerical integration process and ensure the prediction accuracy at the same time. Using this method to estimate the target state of the pursuit and escape game can greatly reduce the amount of calculation, and effectively solve the problem of the explosion of the calculation amount of the Monte Carlo method. The simulation results show that the calculation accuracy of this method is close to that of the traditional Monte Carlo method. When the maximum error is less than 1 micron, the calculation amount of the algorithm in this paper is 22.12-65.31 times higher than the calculation efficiency of the Monte Carlo method, which can effectively predict the target spacecraft real-time status. The simulation results verify the effectiveness of the method proposed in this paper.
AB - In the process of the spacecraft's pursuit and escape game, it is important to predict the motion state of the target quickly and accurately. However, in the process of spacecraft pursuit game, the dynamics of spacecraft targets show complex nonlinear characteristics under a variety of perturbation. It is very difficult to predict the motion state information because of the noise or the uncertain parameters in the observation. In general, Monte Carlo method is used to estimate the motion state of the target when there is uncertainly. Monte Carlo method analyzes the target state information by traversing uncertain parameters and large-scale repetitive numerical integration, which means the Monte Carlo method is very expensive. To solve this problem, this paper proposes a semi-analytical spacecraft target state estimation method based on polynomial approximation. This method divides the spacecraft dynamic parameters with parameter uncertainty into a small number of nominal point parameters and a large number of other associated point parameters. Orbit prediction for nominal point parameters is solved by numerical integration to ensure prediction accuracy. For other related point state prediction problems, the polynomial Taylor expansion semi-analytical method is used to solve the problem, which can greatly reduce the repeated numerical integration process and ensure the prediction accuracy at the same time. Using this method to estimate the target state of the pursuit and escape game can greatly reduce the amount of calculation, and effectively solve the problem of the explosion of the calculation amount of the Monte Carlo method. The simulation results show that the calculation accuracy of this method is close to that of the traditional Monte Carlo method. When the maximum error is less than 1 micron, the calculation amount of the algorithm in this paper is 22.12-65.31 times higher than the calculation efficiency of the Monte Carlo method, which can effectively predict the target spacecraft real-time status. The simulation results verify the effectiveness of the method proposed in this paper.
KW - Monte Carlo method
KW - polynomial approximation
KW - pursuit and escape game
KW - state prediction
KW - uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85129556677&partnerID=8YFLogxK
U2 - 10.1109/ISAS55863.2022.9757284
DO - 10.1109/ISAS55863.2022.9757284
M3 - 会议稿件
AN - SCOPUS:85129556677
T3 - 2022 5th International Symposium on Autonomous Systems, ISAS 2022
BT - 2022 5th International Symposium on Autonomous Systems, ISAS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Symposium on Autonomous Systems, ISAS 2022
Y2 - 8 April 2022 through 10 April 2022
ER -