Neural networks filter for hybrid navigation of formation flying spacecraft in deep space

Hui Li; Qinyu Zhang; Naitong Zhang

doi:10.1117/12.773339

10 November 2007 Neural networks filter for hybrid navigation of formation flying spacecraft in deep space

Hui Li, Qinyu Zhang, Naitong Zhang

Proceedings Volume 6795, Second International Conference on Space Information Technology; 67950Z (2007) https://doi.org/10.1117/12.773339
Event: Second International Conference on Spatial Information Technology, 2007, Wuhan, China

Abstract

Autonomous navigation of spacecrafts is of a difficulty task, however which is a must in future deep space exploration. With multiple spacecrafts flying in space, this aim can be achieved by formation flying spacecrafts utilizing ITDOA and IDD methods, which can locate the position of earth-station from one-way uplink signals in the FFS coordinate, and by way of conversion of coordinates, the position of FFS is achieved in ECEF coordinate. The ability of neural network filter in navigation to extract position of spacecrafts from random measuring noise of signal arrival time and Doppler shift is studied with different radius of FFS and surveying parameters. The NN filter used by spacecraft group is new way of unidirectional autonomous navigation and is of highly precision of hybrid navigation.

Citation Download Citation

Hui Li, Qinyu Zhang, and Naitong Zhang "Neural networks filter for hybrid navigation of formation flying spacecraft in deep space", Proc. SPIE 6795, Second International Conference on Space Information Technology, 67950Z (10 November 2007); https://doi.org/10.1117/12.773339

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