Algorithm of space track segment association under distributed star sensor

被引：1
|
作者：
Huang Q. [1 ]
Zhang Y. [1 ]
Feng F. [1 ]
机构：
[1] Department of Aerospace Science and Technology, Aerospace Engineering University, Beijing
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2020年 / 42卷 / 05期
关键词：
Fuzzy association; Star sensor; Target association; Track segment;
D O I：
10.3969/j.issn.1001-506X.2020.05.06
中图分类号：
学科分类号：
摘要：
In view of the characteristic that the space targets can be observed intermittently by distributed star sensors, the observation information association of the space target in different time periods under star sensors is taken as the premise of accurate space object calibration based on the star sensors. The space target motion characteristic is considered. Combined with the characteristics of the space traget motion, on the basis of the previous two threshold fuzzy correlation, the track segment association algorithm of the space target under the distributed star sensor is proposed by adding the normal vector constraint of orbit plane, filtering the candidate association objects and simplifying the cost of association operation. Through the simulation, the divergence of the track extrapolation error with time is analyzed under six sets of noise levels. And the reference value of the adjustment coefficient in the fuzzy membership function is given, which makes the correlation similarity of different targets more significant under long interval. The simulation results show that the correlation accuracy of the proposed algorithm is higher than that of the nearest neighbor and the traditional fuzzy association under certain noise. When the standard deviation of the initial orbital error is 6 km at each axis position and 6 m/s at each axis velocity, the adjacent targets with a minimum phase difference of 0.5° can be distinguished. Correlation accuracy of interval 2 h is 98%, and the correlation accuracy is 90% of 7 h interval. © 2020, Editorial Office of Systems Engineering and Electronics. All right reserved.
引用
收藏
页码：1007 / 1013
页数：6
相关论文
共 29 条
  • [1] Regan D., Modular neural network tasking of space situational awareness systems, Proc.of the Advanced Maui Optical and Space Surveillance Technologies Conference, (2018)
  • [2] He Y.Y., Wang H.L., High-precision star point extraction method for star sensor star map, Journal of Harbin Institute of Technology, 51, 4, pp. 99-106, (2019)
  • [3] Feng F., Li H.N., Zhang Y.S., Et al., An improved synchronized orbit determination method based on distributed star sensors, Proc.of the 69th International Astronautical Congress, (2018)
  • [4] Madsen G.J., Bland P.A., Bold M., Et al., FireOPAL: technical performance and first results, Proc.of the Advanced Maui Optical and Space Surveillance Technologies Conference, (2018)
  • [5] Aziz A.M., A new nearest-neighbor association approach based on fuzzy clustering, Aerospace Science and Technology, 26, 1, pp. 87-97, (2013)
  • [6] Fan E., Xie W.X., Liu Z.X., Et al., Multi-target tracking based on fuzzy information processing in sensor network, Command Information Systems and Technology, 5, 6, pp. 15-21, (2014)
  • [7] Yoon K., Song Y., Jeon M., Multiple hypothesis tracking algorithm for multi-target multi-camera tracking with disjoint views, IET Image Processing, 12, 7, pp. 1175-1184, (2018)
  • [8] Zhu H., Wang M., Yuen K.V., Et al., Track-to-track association by coherent point drift, IEEE Signal Processing Letters, 24, 5, pp. 643-647, (2017)
  • [9] Coraluppi S., Carthel C., Coon A., An MHT approach to multi-sensor passive sonar tracking, Proc.of the 21st International Conference on Information Fusion (FUSION), pp. 480-487, (2018)
  • [10] Liu Y., Liu J., Sun S., Et al., Multi-sensor fuzzy data interconnection based on velocity azimuth constraint, Systems Engineering and Electronics, 38, 9, pp. 2040-2047, (2016)
123