Relativistic Navigation Method for Deep Space Probes

XIONG Kai, WEI Chunling, LI Liansheng, ZHOU Peng

PDF(1771 KB)
PDF(1771 KB)
Journal of Deep Space Exploration ›› 2023, Vol. 10 ›› Issue (2) : 140-150. DOI: 10.15982/j.issn.2096-9287.2023.20230011
Topic: Celestial Navigation Technology for Deep Space Exploration
Topic: Celestial Navigation Technology for Deep Space Exploration

Relativistic Navigation Method for Deep Space Probes

  • XIONG Kai, WEI Chunling, LI Liansheng, ZHOU Peng
Author information +
History +

Abstract

An autonomous navigation method based on the observations of the relativistic perturbations for deep space probes is presented in this paper. The relativistic perturbations including the stellar aberration and the starlight gravitational deflection are new type of celestial navigation measurement,which can provide the kinematic state information of the deep space probes in the inertial frame. In the relativistic navigation system,the position and velocity vectors of the deep space probes,and the measurement bias of the optical sensor can be estimated through measuring the inter-star angle perturbed by the stellar aberration and the gravitational deflection of light with an optical sensor for LOS (line-of-sight) direction with extremely high accuracy. In this paper,the state equation and measurement equation for the design of the navigation filter and the navigation performance evaluation are established. The feasibility of the relativistic navigation method for deep space probes is investigated via the calculation of the Cramer-Rao Lower Bound (CRLB). In addition,the self-learning strategy of the navigation filter is designed to enhance the relativistic navigation performance. It is illustrated through the numerical simulation that,for a Mars-circling probe,the position error of the relativistic navigation method is on the order of 100 m with the inter-star angle measurement accuracy of 1 mas.

Keywords

deep space exploration / celestial navigation / relativistic perturbations / Q-learning extended Kalman filter

Cite this article

Download citation ▾
XIONG Kai, WEI Chunling, LI Liansheng, ZHOU Peng. Relativistic Navigation Method for Deep Space Probes. Journal of Deep Space Exploration, 2023, 10(2): 140‒150 https://doi.org/10.15982/j.issn.2096-9287.2023.20230011

References

[1] 房建成,宁晓琳. 深空探测器自主天文导航方法[M]. 西安:西北工业大学出版社,2010.
[2] 王大轶,黄翔宇,魏春岭. 基于光学成像测量的深空探测自主控制原理与技术[M]. 北京:中国宇航出版社,2012.
[3] 崔平远,高艾,朱圣英. 深空探测器自主导航与制导[M]. 北京:中国宇航出版社,2016.
[4] 张伟,许俊,黄庆龙,等. 深空天文自主导航技术发展综述[J]. 飞控与探测,2020,3(4):8-16
ZHANG W,XU J,HUANG Q L,et al. Survey of autonomous celestial navigation technology for deep space[J]. Fight Control & Detection,2020,3(4):8-16
[5] 徐瑞,李朝玉,朱圣英,等. 深空探测器自主规划技术研究进展[J]. 深空探测学报(中英文),2021,8(2):111-123
XU R,LI Z Y,ZHU S Y,et al. Research progress of autonomous planning technology for deep space probes[J]. Journal of Deep Space Exploration,2021,8(2):111-123
[6] CHRISTIAN J A. Optical navigation using planet's centroid and apparent diameter in image[J]. Journal of Guidance,Control,and Dynamics,2015,38(2):192-204
[7] FRANZESE V,TOPPUTO F. Optimal beacons selection for deep-space optical navigation[J]. The Journal of the Astronautical Sciences,2020,67:1775-1792
[8] ZHU S Y,XIU Y,ZHANG N,et al. Crater-based attitude and position estimation for planetary exploration with weighted measurement uncertainty[J]. Acta Astronautica,2020,176:216-232
[9] OGAWA N,TERUI F,MIMASU Y,et al. Image-based autonomous navigation of Hayabusa2 using artificial landmarks:the design and brief in-flight results of the first landing on asteroid Ryugu[J]. Astrodynamics,2020,4(2):89-103
[10] ELY T A,SEUBERT J,BRADLEY N,et al. Radiometric autonomous navigation fused with optical for deep space exploration[J]. The Journal of the Astronautical Sciences,2021,68:300-325
[11] SHEIKH S I,PINES D J,RAY P S,et al. Spacecraft navigation using X-ray pulsars[J]. Journal of Guidance,Control,and Dynamics,2006,29(1):49-63
[12] WANG Y,ZHENG W,ZHANG D,et al. X-ray pulsar-based navigation method using one sensor and modified time-differenced measurement[J]. Proceedings of the Institution of Mechanical Engineers,Part G:Journal of Aerospace Engineering,2019,233(1):299-309
[13] LIU J,NING X L,MA X,et al. Geometry error analysis in solar Doppler difference navigation for the capture phase[J]. IEEE Transactions on Aerospace and Electronic Systems,2019,55(5):2556-2567
[14] NING X,CHAO W,HUANG Y,et al. Spacecraft autonomous navigation using the Doppler velocity differences of different points on the solar disk[J]. IEEE Transactions on Aerospace and Electronic Systems,2020,56(6):4615-4625
[15] CHRISTIAN J A. StarNAV:autonomous optical navigation of a spacecraft by the relativistic perturbation of starlight[J]. Sensors,2019,19:4064
[16] YUCALAN D,PECK M. A static estimation method for autonomous navigation of relativistic spacecraft[C]//Proceedings of IEEE Aerospace Conference. Big Sky,MT,USA:IEEE,2019.
[17] CALABRO E. Relativistic aberrational interstellar navigation[J]. Acta Astronautica,2011,69:360-364
[18] XIONG K,WEI C. Integrated celestial navigation for spacecraft using interferometer and Earth sensor[J]. Proceedings of the Institution of Mechanical Engineers,Part G:Journal of Aerospace Engineering,2020,234(16):2248-2262
[19] XIONG K,WEI C,ZHOU P. Integrated autonomous optical navigation using Q-learning extended Kalman filter[J]. Aircraft Engineering and Aerospace Technology,2022,94(6):848-861
[20] WEI Q,LEWIS F L,SUN Q,et al. Discrete-time deterministic Q-learning:a novel convergence analysis[J]. IEEE Transactions on Cybernetics,2017,47(5):1224-1237
[21] 熊凯,魏春岭,郭建新. 航天器导航滤波器设计方法[M]. 北京:中国宇航出版社,2022
[22] SUTTON R S,BARTO A G. Reinforcement learning:an Introduction[M]. London,England:MIT Press,2018.
[23] LEI M,WYK B J,QI Y. Online estimation of the approximate posterior Cramer-Rao lower bound for discrete-time nonlinear filtering[J]. IEEE Transactions on Aerospace and Electronic Systems,2011,47(1):37-57
[24] RISTIC B,FARINA A,BENVENUTI D,et al. Performance bounds and comparison of nonlinear filters for tracking a ballistic object on re-entry[J]. IEE P-Radar Sonar Navigation,2003,150:65-70
[25] DONG G,ZHU Z H. Autonomous robotic capture of non-cooperative target by adaptive extended Kalman filter based visual servo[J]. Acta Astronautica,2016,122:209-218
[26] XIONG K,WEI C,LIU L. Robust extended Kalman filtering for nonlinear systems with stochastic uncertainties[J]. IEEE Transactions on System Man & Cybernetics,Part A,2010,40(2):399-405
PDF(1771 KB)

Accesses

Citations

Detail

Sections
Recommended

/