Improved geometric positioning method with constellation configuration screening

被引：0
作者：
Yang, Xinyu [1 ]
Zeng, Xiangyuan [1 ]
Du, Huajun [2 ]
Liu, Tianci [1 ]
Yang, Haoan [1 ]
Li, Jie [2 ]
机构：
[1] Beijing Institute of Technology, School of Automation, Beijing
[2] National Key Laboratory of Science and Technology on Aerospace Intelligence Control, Beijing Aerospace Automatic Control Institute, Beijing
来源：
Qinghua Daxue Xuebao/Journal of Tsinghua University | 2024年 / 64卷 / 11期
关键词：
configuration screening; constellation configuration; geometric positioning; perspective-n-point problem;
D O I：
10.16511/j.cnki.qhdxxb.2024.26.049
中图分类号：
学科分类号：
摘要：
[Objective] As the number of low earth orbit(LEO) satellites increases, the applicability of optical navigation technology, which uses these satellites as optical information sources, is continually improving. When three or more satellites are simultaneously observed within the optical field of view, the perspective-n-point (PnP) positioning method can be used for pose estimation and positioning. The PnP problem was initially used primarily for camera calibration. Currently the applications of the PnP problem have expanded to various engineering tasks, including simultaneous visual localization and mapping, spatial noncooperative target pose estimation, and the critical stages of rendezvous and docking. However, the PnP algorithm struggles with low positioning accuracy over long distances. Therefore, it is necessary to study the spatial geometric positioning problem when observing satellites at relative distances exceeding hundreds of kilometers. [Methods] This study begins by selecting positioning data sources and statistically investigating the impact of constellation configuration within the optical field of view on positioning errors. The relationships between the constellation area and positioning errors, the geometric angle and positioning errors, and the relationship between the orbit height distribution and positioning errors are analyzed. Through this analysis, the primary influencing indicators are identified as the area, angle, and distance indicators. The distance indicator, in particular, represents the three-dimensional information of the configuration, which cannot be characterized by position dilution of precision (PDOP). To unify the measurement space of each indicator, the indicators are normalized, and the entropy weight method is used to calculate the weight of each indicator. An evaluation function for the constellation configuration is established to assess the configuration availability. The availability distribution is statistically analyzed to determine the evaluation criteria. Finally, using the calculated availability, configurations that are less affected by image noise are selected for the PnP pose calculation. In addition, the difference between the PDOP and positioning error is presented, and the PnP pose is estimated after the configuration is evaluated. [Results] Taking the P3P problem as an example, the positioning error was smaller when the distribution area of the three satellites was larger and more dispersed. According to the proposed screening method, positioning accuracy was improved by more than 50% compared with the situation without screening. Additionally, the configuration positioning accuracy was improved by approximately 37% compared with that of the PDOP-optimized configuration. [Conclusions] Constellation satellites enhance space navigation information sources. Configuration screening effectively improves the accuracy of PnP geometric positioning over long distances, thereby introducing a new concept for long-distance optical navigation. © 2024 Tsinghua University. All rights reserved.
引用
收藏
页码：1979 / 1986
页数：7
相关论文
共 19 条
  • [1] WANG Y, LONG D Y, LUO S, Et al., Constellation coverage and application analysis of Starlink system, Aerospace China, 8, pp. 43-47, (2021)
  • [2] PUMAROLA A, VAKHITOV A, AGUDO A, Et al., PL-SLAM: Real-time monocular visual SLAM with points and lines, IEEE International Conference on Robotics and Automation, (2017)
  • [3] MARCHAND E, UCHIYAMA H, SPINDLER F., Pose estimation for augmented reality: A hands-on survey, IEEE Transactions on Visualization and Computer Graphics, 22, 12, pp. 2633-2651, (2016)
  • [4] XU J J, SONG B, YANG X, Et al., An improved deep keypoint detection network for space targets pose estimation, Remote Sensing, 12, 23, (2020)
  • [5] WANG Q Y, WANG Z Y., Position and pose measurement of spacecraft based on monocular vision, Journal of Applied Optics, 38, 2, pp. 250-255, (2017)
  • [6] LIU H Q, GUAN X J, LUO S B, Et al., Vision inertial fusion based on BP neural network for aircraft autonomous positioning, 2020 International Conference on Guidance, Navigation and Control, (2020)
  • [7] JOSPIN L, STOVEN-DUBOIS A, CUCCI D A., Photometric long-range positioning of LED targets for cooperative navigation in UAVs, Drones, 3, 3, (2019)
  • [8] LIU C L, LIU L, HU G F, Et al., A P3P problem solving algorithm for landing vision navigation, Navigation Positioning and Timing, 5, 1, pp. 64-67, (2018)
  • [9] LIU J Y., Theory and application of navigation system, (2010)
  • [10] ANSARI K, JAMJAREEGULGARN P., Effect of weighted PDOP on performance of linear Kalman filter for RTK drone data, IEEE Geoscience and Remote Sensing Letters, 19, (2022)
12