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	GNSS Single Point Fast Positioning Based on BP Neural Network
	Meng, Fanchen ; Wang, Shan ; Zhu, Bocheng
	2015
英文摘要	In order to speed up the realization of Global Navigation Satellite System (GNSS) space position and velocity, a novel technique is proposed for GNSS orbit fast fitting in this paper, which contributes to the acquisition of GNSS satellite signal significantly. The ephemeris orbit model is imminent by different fitting algorithm and polynomial interpolation is utilized for the purpose. Moreover, different degrees of Lagrange and Hermite polynomial and proposed Back-Propagation (BP) neural network are compared for orbit approximation. We creatively make full use of neural network learning and training function for better estimation of derivable satellite motion trajectory. It is here extended by analysis of satellite geometry distribution to ensure the robust navigation of receivers. Theoretical analysis and simulation results demonstrate that the new technique yields better performance for satellite orbit resolving compared with traditional schemes and along with Cooley-Tukey Fast Fourier Transform (FFT) beneficial properties, it attains high efficiency compared to ephemeris direct evaluation.; CPCI-S(ISTP); 935-946
语种	英语
出处	INTERNATIONAL CONFERENCE ON SUSTAINABLE ENERGY AND ENVIRONMENT PROTECTION (ICSEEP 2015)
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/424291]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Meng, Fanchen,Wang, Shan,Zhu, Bocheng. GNSS Single Point Fast Positioning Based on BP Neural Network. 2015-01-01.

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