Observability degrees based lightweight construction method for sequential features of autonomous navigation

doi:10.16708/j.cnki.1000-758X.2025.0095

Chinese Space Science and Technology ›› 2025, Vol. 45 ›› Issue (6): 133-140.doi: 10.16708/j.cnki.1000-758X.2025.0095

Previous Articles Next Articles

Observability degrees based lightweight construction method for sequential features of autonomous navigation

LI Jiaxing¹，WANG Dayi^1,*，XU Chao²，LI Maodeng²，DONG Tianshu¹，FU Fangzhou³

1.Beijing Institute of Spacecraft System Engineering，Beijing 100094，China
2.Beijing Institute of Control Engineering，Beijing 100094，China
3.School of Aeronautics and Astronautics，Sun Yat-sen University，Shenzhen 518107，China

Received:2024-08-12 Revision received:2024-12-02 Accepted:2025-01-04 Online:2025-11-17 Published:2025-12-01

Abstract

Abstract: Autonomous planetary landing navigation based on sequential images is constrained by the limited computational resources onboard the lander, making it difficult to process and match a large number of image features. This paper designs observability degrees from both spatial and temporal dimensions. A spatial configuration observability degree for landmarks is introduced to optimize the selection of the best feature locations within the sequence, while a depth estimation error observability degree is proposed to determine the optimal time intervals between features. These observability degrees are used to guide the construction of lightweight sequential features with low computational burden. The proposed observability degrees avoid complex matrix operations, making them suitable for onboard autonomous selection of landmarks and feature sequences. Simulation results demonstrate that, compared to traditional methods, the lightweight sequential feature construction method significantly reduces computational load and the number of image processing operations during landmark selection, thereby effectively enhancing the autonomous navigation capability of the lander.

Key words: planetary landing, autonomous navigation, sequential image, observability degree, lightweight

LI Jiaxing, WANG Dayi, XU Chao, LI Maodeng, DONG Tianshu, FU Fangzhou. Observability degrees based lightweight construction method for sequential features of autonomous navigation[J]. Chinese Space Science and Technology, 2025, 45(6): 133-140.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: https://journal26.magtechjournal.com/kjkxjs/EN/10.16708/j.cnki.1000-758X.2025.0095

https://journal26.magtechjournal.com/kjkxjs/EN/Y2025/V45/I6/133

[1]	ZHOU Bochao, LI Yong, ZHANG Ai, CUI Shihang. Observability analysis of satellite autonomous orbit determination with modeling and measurement errors [J]. Chinese Space Science and Technology, 2023, 43(3): 25-34.
[2]	ZHOU Bochao, LI Yong. A multi-stage filter for autonomous navigation of formation satellites based on inter-satellite measurements [J]. Chinese Space Science and Technology, 2022, 42(3): 124-131.
[3]	DING Taowei, SHUAI Ping, HUANG Liangwei, ZHANG Xinyuan. An autonomous orbit determination algorithm of XPNAV-1 based on extended Kalman filtering [J]. Chinese Space Science and Technology, 2021, 41(1): 13-21.
[4]	YE Zipeng, ZHOU Qingrui, WANG Hui. Research on autonomous navigation method for deep space exploration spacecraft in cruise phase [J]. Chinese Space Science and Technology, 2020, 40(5): 53-60.
[5]	TANG Qingyuan, WANG Xiaolei. Autonomous navigation based on Deimos lightofsight for Mars approaching phase [J]. Chinese Space Science and Technology, 2019, 39(6): 38-.
[6]	DENG Guanghui, LIAO Zhuofan, ZHU Rong, WANG Jiongqi. Spacecraft autonomous navigation with cubature Kalman filter based on sun-earth-moon information#br# [J]. Chinese Space Science and Technology, 2018, 38(1): 70-76.
[7]	DANG Jian-Jun, LUO Jian-Jun, WAN Yan-Hui. Method of On-orbit Calibrating Scale Factor of Accelerometer Based on Lever Arm Effect [J]. Chinese Space Science and Technology, 2013, 33(2): 19-24.
[8]	SHI Heng, XU Shi-Jie. Autonomous Navigation and In-orbit Parameter Calibration for Continuous Low Thrust Spacecraft [J]. Chinese Space Science and Technology, 2012, 32(4): 62-70.
[9]	LUO Nan, XU Lu-Ping, ZHANG Hua. Method of Autonomous Celestial Navigation Based on UKF and Information Fusion [J]. Chinese Space Science and Technology, 2012, 32(2): 1-9.
[10]	Song Chen Han Chao Geng Jianzhong(School of Space Technology,Beijing University of Areonautics and Astronautics,Beijing 100191). Application of Adaptive Particle Filtering in Ultraviolet Sensors [J]. Chinese Space Science and Technology, 2009, 29(01): 32-40.
[11]	Zheng Wei Sun Shouming Tang Guojian(College of Aerospace and Material Engineering,National University of Defense Technology,Changsha 410073). Principle of Deep Space Autonomous Navigation Based on X-ray Pulsars [J]. Chinese Space Science and Technology, 2008, 28(05): 1-6.
[12]	Wang Peng Zhang Yingchun (Research Center of Satellite Technology,Harbin Institute of Technology, Harbin 150080). Federal Filter Method of Autonomous Orbit Determination for Low-Earth-Orbit Satellite [J]. Chinese Space Science and Technology, 2008, 28(03): 15-21+71.
[13]	Liu Yong Xu Shijie (School of Astronautics,Beijing University of Aeronautics and Astronautics,Beijing 100083). Polynomial Interpolation Filtering for Relative Navigation of Spacecraft on Elliptical Orbit [J]. Chinese Space Science and Technology, 2008, 28(03): 37-44.
[14]	Shuai Ping Li Ming Chen Shaolong Wu Yifan Zhang Chunqing(China Academy of Space Technology,Beijing 100094). Autonomous Navigation for Navigation Satellites Based on X-ray Pulsars [J]. Chinese Space Science and Technology, 2008, 28(02): 1-7.
[15]	Qiao Li Liu Jianye Xiong Zhi Zheng Guanglou (Navigation Research Center,Nanjing University of Aeronautics and Astronautics,Nanjing 210016). X-ray Pulsars Based Autonomous Navigation Scheme for the Deep Space Explorer [J]. Chinese Space Science and Technology, 2007, 27(06): 1-6+18.

Observability degrees based lightweight construction method for sequential features of autonomous navigation

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments