A New Multiple Information Fusion Approach for Astronomical Navigation using Markov Random Field algorithm

2019 5th International Conference on Energy Materials and Environment Engineering

能源学;生态环境科学

Wang, Wei^1^2 ; Zhang, Wei^2 ; Ma, Guangfu^1 ; Du, Kaiyue^3 ; Wang, Shifeng^3

School of Astronautics, Harbin Institute of Technology, Harbin

150001, China^1

Shanghai Institute of Satellite Engineering, Shanghai

201109, China^2

National Demonstration Center for Experimental Optoelectronic Engineering Education, School of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun, Jilin

130022, China^3

关键词: Autonomous navigation;    Deep-space exploration;    Deep-space navigation;    Markov Random Fields;    Measurement information;    Multiple information fusions;    Navigation in formation;    Real-time navigation;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/295/3/032059/pdf
DOI  :  10.1088/1755-1315/295/3/032059

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Autonomous navigation is one of the key technologies to ensure the implementation of deep space exploration mission. Astronomical angle measurement information and stellar spectral velocity measurement information are usually used to estimate the real-time navigation state. According to the characteristics and requirements of deep space navigation, this paper proposes a novel method for information fusing using Markov Random Field approach, which integrates the navigation information including angle measurement, velocity measurement and combination of angle and velocity measurement, to realize continuous autonomous, real-time and high-precision navigation of deep space exploration. Considering the background of Mars exploration engineering mission, the observability and precision of the navigation method are analysed. The simulation results show that the method based on Markov Random Field can greatly improve the observability of the navigation system, effectively suppress the influence of measurement error, further improve the accuracy of navigation estimation, enhance the reliability of the navigation system, and provide a new technical approach to realize the high precision autonomous navigation of deep space exploration.